Product Design Methods

**Chapter 1**

**Abstract**

**1. Introduction**

**3**

a Novel Product

*Hrayr Darbinyan*

Task-Based Conceptual Design of

A novel task-based conceptual design method introduced around a decade ago has been presented from its most characteristic points including the general idea, usage and modification of previous art, usage and modification of independent sets of functional and mechanical means for implementation of those functions, creation of intermediate mechanical-functional sets supporting the development of new structures like models, visualization of the design process, and so on. The current paper aims to reveal a non-computerized graphically visualized set of actions covering all the above-mentioned major steps of the suggested methodology. The success of synthesizing action greatly depends on the method of creation submechanisms or virtual mechanisms, which are making possible visualization and consideration intermediate structures helping to identify and implement a necessary function. The method of creating of such subcategories and application of elementary movements or set of links for explaining or satisfying demanded set of functions could be considered the main methodical novelty and strength of proposed conceptual design method. Two examples are included: the first reinvention of a known tool—Locking Pliers from database

and second synthesis of a novel hand tool—Adjustable Nut Wrench.

The conceptual phase of design still remains as the most challenging and less understood steps of general mechanical design. The difficulties of its description and formulization are coming from first the nature of conceptual design, implying search of a novel structure with novel properties among theoretically large number of candidate solutions, and secondly from the individual nature of designing process, depending on design tradition, skill, and experience of the designer. Design process being very attractive and creative by its definition may bring the designer more results and satisfaction if organized in a way to free the designer from the routine task of checking a large number of options with necessary efforts of visualization. The designer will greatly win if only decision-making duty from a limited number of candidate solutions will be left on his side. Combinational methods widely used for novel structure solution search are effective for the automatic organization of search process with minimum human involvement; however they are providing solutions for a key or major function, only suggesting modifications within fixed topology with randomly generated accompanying functions with a high probability of negative functions among them. The fact of single or key function consideration in

**Keywords:** conceptual, design, task, structure, modification

## **Chapter 1**

## Task-Based Conceptual Design of a Novel Product

*Hrayr Darbinyan*

## **Abstract**

A novel task-based conceptual design method introduced around a decade ago has been presented from its most characteristic points including the general idea, usage and modification of previous art, usage and modification of independent sets of functional and mechanical means for implementation of those functions, creation of intermediate mechanical-functional sets supporting the development of new structures like models, visualization of the design process, and so on. The current paper aims to reveal a non-computerized graphically visualized set of actions covering all the above-mentioned major steps of the suggested methodology. The success of synthesizing action greatly depends on the method of creation submechanisms or virtual mechanisms, which are making possible visualization and consideration intermediate structures helping to identify and implement a necessary function. The method of creating of such subcategories and application of elementary movements or set of links for explaining or satisfying demanded set of functions could be considered the main methodical novelty and strength of proposed conceptual design method. Two examples are included: the first reinvention of a known tool—Locking Pliers from database and second synthesis of a novel hand tool—Adjustable Nut Wrench.

**Keywords:** conceptual, design, task, structure, modification

## **1. Introduction**

The conceptual phase of design still remains as the most challenging and less understood steps of general mechanical design. The difficulties of its description and formulization are coming from first the nature of conceptual design, implying search of a novel structure with novel properties among theoretically large number of candidate solutions, and secondly from the individual nature of designing process, depending on design tradition, skill, and experience of the designer. Design process being very attractive and creative by its definition may bring the designer more results and satisfaction if organized in a way to free the designer from the routine task of checking a large number of options with necessary efforts of visualization. The designer will greatly win if only decision-making duty from a limited number of candidate solutions will be left on his side. Combinational methods widely used for novel structure solution search are effective for the automatic organization of search process with minimum human involvement; however they are providing solutions for a key or major function, only suggesting modifications within fixed topology with randomly generated accompanying functions with a high probability of negative functions among them. The fact of single or key function consideration in

combinational search dramatically lowers its methodical strength because any design process is valued for providing a multifunctional solution but not valued for the fact of generation from a single topology. A novel conceptual design is generally preceded by an act of making a decision which is possible after managing large data of previous knowledge and search of candidate solutions of the current design process. The success of large data management depends on the success of turning large data into small-sized easily manageable portions of information or models. From category point, the models should serve both categories involved, namely, functions and mechanism to provide interdependence and simultaneous consideration of those two, and from application point, they need to serve both classic actions of design—synthesis and analysis—including such segments of design as database analysis and creation of supplementary virtual mechanisms with the latest further update into structures, satisfying the given design tasks. That's practically an impossible task to understand and manage a designer's own plan during his or her efforts to create a novel mechanism. Commonly a designer who puts an aim to create a new mechanical structure or to update an existing one relies on proved by own practice and experience approaches and scenarios which could be basically different from each other and normally not shared with designers' community. For the past few decades, due to growing demand on fresh products with advantageous proprieties and because of wider application of digital technologies, the challenge of better organization of conceptual design process becomes more actual, and this demand was satisfied by several approaches and methodologies. The task-based design methods can be conventionally divided into methodologies based mostly on human participation or on computer-aided methods with minimum involvement of human factor. Some examples for the second group of task-based design methodologies are quite successful when directing a designer to organize a new product development with novel properties [1–3]. Very popular and classical methods [4, 5] of splitting mechanical components from functional ones have clear abstraction and visualization means and require consideration of a large number of candidate solutions in an attempt to isolate a workable and optimal one. A fundamental publication [6] is using analyses of the vast engineering database as a source for a novel product design, where the search trend implies consideration of either combination of various movements of basic links or direct search of solutions among existing solutions. Insufficient level of abstraction and visualization narrows the opportunities of processing and getting optimal results among mechanical means, having required functions and properties. Any design methodology can be evaluated by the number of essential design tasks considered during a mechanism synthesis process and distribution of those tasks along with steps of conceptual design: more tasks involved provides wider and full satisfaction of design aim with a maximum number of demanded properties of a novel product. When following [4, 5] methodologies, there is a great risk of missing and/or canceling consideration of essential features on one side and necessity of implementing of an exhausting search of candidate solution on the other. The largely popularized method of Theory of Inventive Problem Solving (TRIZ/TIPS) is quite effective for finding solutions of conceptual design and resolving invention tasks [7, 8]. A contradiction matrix and set of 40 creative/inventive tools are used for developing special auxiliary structures—VEPOLS (a Russian abbreviation of substance + field)—as a model of future novel product for revealing new and neutralizing the harmful functions. That's very common in mechanical engineering practice that an innovative solution surfs out once a limited set of various requirements are considered simultaneously, thus facilitating the creation of a decision-making situation. In the TIPS case, the fact of usage of a cumbersome set of 40 tools and no necessary relation of this set to the essence of the main problem generally may lead to comprehensive search with a broad number of possible results.

A task-based methodology of conceptual design [9] developed as a result of long-term engineering experience has proven its efficiency in the development of numerous and various mechanical devices based on interdependent and direct consideration of two sets of components—mechanical and functional—in a state when those components are processed to design models, facilitating their application or further modification for satisfying a current design task, while the remaining ones are planned to be satisfied by similar modification actions. Work models are developed at consecutive steps of the design process which may have different

In Sections 2.1 and 2.2, flat graphs are used for describing key mechanical and functional models; then the same graphs are used for visualizing modification (expansion and squeezing) of those models for serving different design needs. In Section 2.3 the development of local mechanical and functional models is presented; Section 2.4 covers developments for a solution means, namely, resources of database and resources of synthesis tools and similar developments for functional sets. Section 3 is, for example, relating to the reinvention of a known tool—locking pliers —from patent database, and finally, Section 4 is for the set of design cycles of

The task of this paper is to provide clear graphical presentation and visualization

For mechanical categories the graph (**Figure 1**) has two vertices for links *L1* and *L2* related to each other by an edge as a kinematical joint in general or as another relation *R12* in a way that this relation may satisfy or plan satisfaction of demanded function *F12*. In fact, the links *L1* and *L2* are connected through two paths, firstly the edge *F12* represents the function as a subject of satisfaction, while the edge *R12* represents the mechanical or physical means needed for satisfying the demanded function. At the implementation stage, *F12* could be replaced by a physical kine-

For functional categories the graph (**Figure 2**) visualizes the step of getting a translated function *F2* from function *F1*, where edge *T12* stands for a translating operator, while the physical implementation of function *F2* is supposed to be done through mechanical means *M12*, represented by the second edge in the graph (**Figure 2**). That's easy to notice the topological analogy between **Figure 1** and **Figure 2**. Vertices of graph in **Figure 2** also are connected through two paths, firstly by the edge representing the translating operator *T12* providing a child function *F2* which may be implemented in contrary to function *F1* and secondly including second edge *M12* representing the mechanical mean for such implementation. *R12* in square symbol in **Figure 2** stands for the type of relation between functions *F1*

for steps of the proposed approach of conceptual design methodology through mechanisms, functional-mechanical graphs, and set of hierarchized functions to control and manage the process of conceptual design, starting from the task on it and finishing with novel structure satisfying the pre-given tasks on development.

contents depending on the step, level, and scale of the design task.

*Task-Based Conceptual Design of a Novel Product DOI: http://dx.doi.org/10.5772/intechopen.92155*

conceptual design for a specific hand tool: self-adjustable nut wrench.

**2. Main ideas of conceptual design method described by**

**2.1 Key models containing mechanical and functional categories**

**mechanical-functional graphs**

matical joint shown in square symbol as *R12*.

and *F2*.

**5**

**1.1 Tasks and objectives**

*Task-Based Conceptual Design of a Novel Product DOI: http://dx.doi.org/10.5772/intechopen.92155*

combinational search dramatically lowers its methodical strength because any design process is valued for providing a multifunctional solution but not valued for the fact of generation from a single topology. A novel conceptual design is generally preceded by an act of making a decision which is possible after managing large data of previous knowledge and search of candidate solutions of the current design process. The success of large data management depends on the success of turning large data into small-sized easily manageable portions of information or models. From category point, the models should serve both categories involved, namely, functions and mechanism to provide interdependence and simultaneous consideration of those two, and from application point, they need to serve both classic actions of design—synthesis and analysis—including such segments of design as database analysis and creation of supplementary virtual mechanisms with the latest further update into structures, satisfying the given design tasks. That's practically an impossible task to understand and manage a designer's own plan during his or her efforts to create a novel mechanism. Commonly a designer who puts an aim to create a new mechanical structure or to update an existing one relies on proved by own practice and experience approaches and scenarios which could be basically different from each other and normally not shared with designers' community. For the past few decades, due to growing demand on fresh products with advantageous proprieties and because of wider application of digital technologies, the challenge of better organization of conceptual design process becomes more actual, and this demand was satisfied by several approaches and methodologies. The task-based design methods can be conventionally divided into methodologies based mostly on human participation or on computer-aided methods with minimum involvement of human factor. Some examples for the second group of task-based design methodologies are quite successful when directing a designer to organize a new product development with novel properties [1–3]. Very popular and classical methods [4, 5] of splitting mechanical components from functional ones have clear abstraction and visualization means and require consideration of a large number of candidate solutions in an attempt to isolate a workable and optimal one. A fundamental publication [6] is using analyses of the vast engineering database as a source for a novel product design, where the search trend implies consideration of either combination of various movements of basic links or direct search of solutions among existing solutions. Insufficient level of abstraction and visualization narrows the opportunities of processing and getting optimal results among mechanical means, having required functions and properties. Any design methodology can be evaluated by the number of essential design tasks considered during a mechanism synthesis process and distribution of those tasks along with steps of conceptual design: more tasks involved provides wider and full satisfaction of design aim with a maximum number of demanded properties of a novel product. When following [4, 5] methodologies, there is a great risk of missing and/or canceling consideration of essential features on one side and necessity of implementing of an exhausting search of candidate solution on the other. The largely popularized method of Theory of Inventive Problem Solving (TRIZ/TIPS) is quite effective for finding solutions of conceptual design and resolving invention tasks [7, 8]. A contradiction matrix and set of 40 creative/inventive tools are used for developing special auxiliary structures—VEPOLS (a Russian abbreviation of substance + field)—as a model of future novel product for revealing new and neutralizing the harmful functions. That's very common in mechanical engineering practice that an innovative solution surfs out once a limited set of various requirements are considered simultaneously, thus facilitating the creation of a decision-making situation. In the TIPS case, the fact of usage of a cumbersome set of 40 tools and no necessary relation of this set to the essence of the main problem generally may lead to

*Product Design*

comprehensive search with a broad number of possible results.

**4**

A task-based methodology of conceptual design [9] developed as a result of long-term engineering experience has proven its efficiency in the development of numerous and various mechanical devices based on interdependent and direct consideration of two sets of components—mechanical and functional—in a state when those components are processed to design models, facilitating their application or further modification for satisfying a current design task, while the remaining ones are planned to be satisfied by similar modification actions. Work models are developed at consecutive steps of the design process which may have different contents depending on the step, level, and scale of the design task.

In Sections 2.1 and 2.2, flat graphs are used for describing key mechanical and functional models; then the same graphs are used for visualizing modification (expansion and squeezing) of those models for serving different design needs. In Section 2.3 the development of local mechanical and functional models is presented; Section 2.4 covers developments for a solution means, namely, resources of database and resources of synthesis tools and similar developments for functional sets. Section 3 is, for example, relating to the reinvention of a known tool—locking pliers —from patent database, and finally, Section 4 is for the set of design cycles of conceptual design for a specific hand tool: self-adjustable nut wrench.

## **1.1 Tasks and objectives**

The task of this paper is to provide clear graphical presentation and visualization for steps of the proposed approach of conceptual design methodology through mechanisms, functional-mechanical graphs, and set of hierarchized functions to control and manage the process of conceptual design, starting from the task on it and finishing with novel structure satisfying the pre-given tasks on development.

## **2. Main ideas of conceptual design method described by mechanical-functional graphs**

## **2.1 Key models containing mechanical and functional categories**

For mechanical categories the graph (**Figure 1**) has two vertices for links *L1* and *L2* related to each other by an edge as a kinematical joint in general or as another relation *R12* in a way that this relation may satisfy or plan satisfaction of demanded function *F12*. In fact, the links *L1* and *L2* are connected through two paths, firstly the edge *F12* represents the function as a subject of satisfaction, while the edge *R12* represents the mechanical or physical means needed for satisfying the demanded function. At the implementation stage, *F12* could be replaced by a physical kinematical joint shown in square symbol as *R12*.

For functional categories the graph (**Figure 2**) visualizes the step of getting a translated function *F2* from function *F1*, where edge *T12* stands for a translating operator, while the physical implementation of function *F2* is supposed to be done through mechanical means *M12*, represented by the second edge in the graph (**Figure 2**). That's easy to notice the topological analogy between **Figure 1** and **Figure 2**. Vertices of graph in **Figure 2** also are connected through two paths, firstly by the edge representing the translating operator *T12* providing a child function *F2* which may be implemented in contrary to function *F1* and secondly including second edge *M12* representing the mechanical mean for such implementation. *R12* in square symbol in **Figure 2** stands for the type of relation between functions *F1* and *F2*.

**Figure 1.** *Graph model for mechanical set.*

**Figure 2** into the functional edge in **Figure 1**, thus getting combined mechanical functional graph (**Figure 3**), and correspondingly by inserting mechanical contents of the graph (**Figure 1**) into the mechanical edge of the functional graph (**Figure 2**),

**2.2 Models describing modification of mechanical and functional categories by**

Once model graphs (**Figures 1**–**4**) are setting contents (centrally located) and relation (located on the left and right sides) of components of mechanical and functional matrices, expansion and squeezing actions aim to set synthesis preparation step. Those actions aim to establish the necessary scope of the search in a manageable way, avoiding exponential growth of components which leads to an exhausting search of sought solution. For the case of mechanical graph (**Figure 6**), the action of expansion results in necessary multiplication or addition of number of links in the central graph with an indication of mutual relation presented by a generalized symbol of *R18* (**Figure 5**). For the case of a functional graph, the same similar action leads to necessary multiplication or addition of the number of functions generated in an attempt to have the chance of their satisfaction by mechanical graph (**Figure 6**). Symbol *T18* stands for generalized translation operator between

**means of expansion and squeezing**

thus getting combined functional-mechanical graph (**Figure 4**).

**Figure 3.**

**Figure 4.**

**7**

*Combined functional graph.*

*Combined mechanical graph.*

*Task-Based Conceptual Design of a Novel Product DOI: http://dx.doi.org/10.5772/intechopen.92155*

**Figure 2.** *Graph model for functional set.*

It is worthy to note that both models include two initiating components, which in the mechanical model can be interpreted as a necessity of two links for getting a movement between them to implement a function. Analogously in the functional model, the presence of two functions can be interpreted as a necessity for having at least a function next to the initial function to provide its implementation by mechanical means. Generally, a single function also may serve as a task for development; anyhow a two-function model is considered to keep topological similarity of two basic models and also for presenting the translator operator which has the analogy to the relation of the links in the mechanical model in **Figure 3**.

The graphs in **Figures 1** and **2** are confirming the main idea of the proposed method of direct interdependence between mechanical and functional means for the fact of the presence of a functional edge in mechanical graph and presence of a mechanical edge in the functional graph. By the progress of the design process, the edges for functional graphs should be gradually substituted by physical relations between the links, and thus a novel mechanical structure should be generated at the end of design.

Structurally both design components' links and functions are centrally located in both graphs, leaving the left cells for relations between components and the right cells for the purpose of the means of implementation of those relations.

Other characteristic properties of the concept design method—interdependence of mechanical and functional categories and therefore possibility of combined consideration as a condition of target-oriented organization of conceptual design process—can be topologically visualized by the insertion of functional contents from

*Task-Based Conceptual Design of a Novel Product DOI: http://dx.doi.org/10.5772/intechopen.92155*

**Figure 3.** *Combined mechanical graph.*

**Figure 2** into the functional edge in **Figure 1**, thus getting combined mechanical functional graph (**Figure 3**), and correspondingly by inserting mechanical contents of the graph (**Figure 1**) into the mechanical edge of the functional graph (**Figure 2**), thus getting combined functional-mechanical graph (**Figure 4**).

**Figure 4.** *Combined functional graph.*

It is worthy to note that both models include two initiating components, which in the mechanical model can be interpreted as a necessity of two links for getting a movement between them to implement a function. Analogously in the functional model, the presence of two functions can be interpreted as a necessity for having at

least a function next to the initial function to provide its implementation by mechanical means. Generally, a single function also may serve as a task for development; anyhow a two-function model is considered to keep topological similarity of two basic models and also for presenting the translator operator which has the

analogy to the relation of the links in the mechanical model in **Figure 3**.

cells for the purpose of the means of implementation of those relations.

end of design.

**6**

**Figure 1.**

*Product Design*

**Figure 2.**

*Graph model for functional set.*

*Graph model for mechanical set.*

The graphs in **Figures 1** and **2** are confirming the main idea of the proposed method of direct interdependence between mechanical and functional means for the fact of the presence of a functional edge in mechanical graph and presence of a mechanical edge in the functional graph. By the progress of the design process, the edges for functional graphs should be gradually substituted by physical relations between the links, and thus a novel mechanical structure should be generated at the

Structurally both design components' links and functions are centrally located in both graphs, leaving the left cells for relations between components and the right

Other characteristic properties of the concept design method—interdependence of mechanical and functional categories and therefore possibility of combined consideration as a condition of target-oriented organization of conceptual design process—can be topologically visualized by the insertion of functional contents from

## **2.2 Models describing modification of mechanical and functional categories by means of expansion and squeezing**

Once model graphs (**Figures 1**–**4**) are setting contents (centrally located) and relation (located on the left and right sides) of components of mechanical and functional matrices, expansion and squeezing actions aim to set synthesis preparation step. Those actions aim to establish the necessary scope of the search in a manageable way, avoiding exponential growth of components which leads to an exhausting search of sought solution. For the case of mechanical graph (**Figure 6**), the action of expansion results in necessary multiplication or addition of number of links in the central graph with an indication of mutual relation presented by a generalized symbol of *R18* (**Figure 5**). For the case of a functional graph, the same similar action leads to necessary multiplication or addition of the number of functions generated in an attempt to have the chance of their satisfaction by mechanical graph (**Figure 6**). Symbol *T18* stands for generalized translation operator between

## **Figure 5.**

*Graph model for expansion and squeezing of mechanical means.*

functions *F1 and F8*. Graphs (**Figures 5** and **6**) can show the expandable and squeezable nature of both mechanical and functional entities by a simple modification—multiplication of number of links and multiplication of the number of functions. Along with multiplication, the edges of graphs should provide connections for both links and functions, thus confirming the function-based modification for the mechanical graph case and confirming the generation of new functions in the functional graph case. The opposite action of subtracting the links and functions will relate to squeezing case.

**2.3 Development of mechanical and functional models as preparation for**

*Graph for combined expanding-squeezing mechanical-functional model.*

*Task-Based Conceptual Design of a Novel Product DOI: http://dx.doi.org/10.5772/intechopen.92155*

The modification of mechanical and functional sets is following different design goals: search and disclosure of hidden resources—in case of expansion and localization of the task and in case of squeezing. In both cases, interdependent portions of mechanical means and functions are being isolated. Those portions are easily manageable for analyzing the function implementation scenarios and for building a new structure with new properties. As described above isolated portions of mechanical/ functional sets are called models which are concentrating the main problem at a current design state, separating the problem from the general design process.

Localization and isolation of a problem are a widely practiced [3, 6, 7] action in mechanical design, supporting the task concentration, task targeting, and finding a solution. Anyhow the approach based on freedom and possibility of management and modification of two sets of mechanical and functional means in a direct and interdependent way, which opens a large-scale opportunity for finding an optimal solution; avoiding a large-scale exhausting search makes the proposed approach advantageously and effectively different from the abovementioned task localization approaches and design procedures. Thereby, two fragments of links *L3* … *L6* and functions *F3* … *F6* from **Figure 8** are separated and localized from general mechanical *(M)* and functional (*F*) sets. The isolated fragments thus are containing a low number of links and functions, and they are isolated in a way to focus designer attention on a finding of a solution of an isolated or targeted objective function. The isolated fragments of mechanical means contain links which either have the ability

*Graph representation of two localized fragments (models) in mechanical and functional sets.*

**synthesizing action**

**Figure 7.**

**Figure 8.**

**9**

The combination of edges *F12* from **Figure 5** and M12 from **Figure 6** helps to compose combined expanding-squeezing model as shown in **Figure 7**.

Represented modifications of both mechanical and functional means have the ability to disclose the hidden functional resources of the mechanical side and hidden ways of mechanical implementation of the functional side. Accordingly, both models have the ability of concentration of a limited number of links for mechanical side, creating local mechanical models and for a concentration of a limited number of functions for creating a local set of functions subject to implementation.

The case when the mechanical set consists of just one link may be interpreted as the squeezed down the state of set of links which were initially connected by a set of relations and then unified into a single link after consideration and implementation of those functional requirements. The mechanical set (**Figure 5**) can include the entire set of concept design components when the generalized function is substituted by its contents from **Figure 6**.

**Figure 6.** *Graph model for expansion and squeezing of functions.*

*Task-Based Conceptual Design of a Novel Product DOI: http://dx.doi.org/10.5772/intechopen.92155*

**Figure 7.** *Graph for combined expanding-squeezing mechanical-functional model.*

## **2.3 Development of mechanical and functional models as preparation for synthesizing action**

The modification of mechanical and functional sets is following different design goals: search and disclosure of hidden resources—in case of expansion and localization of the task and in case of squeezing. In both cases, interdependent portions of mechanical means and functions are being isolated. Those portions are easily manageable for analyzing the function implementation scenarios and for building a new structure with new properties. As described above isolated portions of mechanical/ functional sets are called models which are concentrating the main problem at a current design state, separating the problem from the general design process.

Localization and isolation of a problem are a widely practiced [3, 6, 7] action in mechanical design, supporting the task concentration, task targeting, and finding a solution. Anyhow the approach based on freedom and possibility of management and modification of two sets of mechanical and functional means in a direct and interdependent way, which opens a large-scale opportunity for finding an optimal solution; avoiding a large-scale exhausting search makes the proposed approach advantageously and effectively different from the abovementioned task localization approaches and design procedures. Thereby, two fragments of links *L3* … *L6* and functions *F3* … *F6* from **Figure 8** are separated and localized from general mechanical *(M)* and functional (*F*) sets. The isolated fragments thus are containing a low number of links and functions, and they are isolated in a way to focus designer attention on a finding of a solution of an isolated or targeted objective function. The isolated fragments of mechanical means contain links which either have the ability

**Figure 8.** *Graph representation of two localized fragments (models) in mechanical and functional sets.*

functions *F1 and F8*. Graphs (**Figures 5** and **6**) can show the expandable and squeezable nature of both mechanical and functional entities by a simple modification—multiplication of number of links and multiplication of the number of functions. Along with multiplication, the edges of graphs should provide connections for both links and functions, thus confirming the function-based modification for the mechanical graph case and confirming the generation of new functions in the functional graph case. The opposite action of subtracting the links and functions

The combination of edges *F12* from **Figure 5** and M12 from **Figure 6** helps to

Represented modifications of both mechanical and functional means have the ability to disclose the hidden functional resources of the mechanical side and hidden ways of mechanical implementation of the functional side. Accordingly, both models have the ability of concentration of a limited number of links for mechanical side, creating local mechanical models and for a concentration of a limited number

The case when the mechanical set consists of just one link may be interpreted as the squeezed down the state of set of links which were initially connected by a set of relations and then unified into a single link after consideration and implementation of those functional requirements. The mechanical set (**Figure 5**) can include the entire set of concept design components when the generalized function is

compose combined expanding-squeezing model as shown in **Figure 7**.

of functions for creating a local set of functions subject to implementation.

will relate to squeezing case.

*Graph model for expansion and squeezing of mechanical means.*

**Figure 5.**

*Product Design*

substituted by its contents from **Figure 6**.

*Graph model for expansion and squeezing of functions.*

**Figure 6.**

**8**

to satisfy the sought function or may gain this ability after proper modification, adding links, chains, grouping links, etc.

Localization of *mechanical and functional* fragments relates not only to neighbor components as it stated for convenience and simplicity but to any combination of components from **Figures 5** and **6**.

setting of objectives for design and ends with the structural diagram. Each design cycle is provided by explanations cited in **Figures 10**–**17**. Each design cycle shows the degree of implementation of design goal starting from 0 and ending

*This is a task for synthesis; four basic functions are set as subject for implementation. Lock an object, provide adjustable thickness of locked object, develop necessary pressure for locking, and release the object if necessary.*

*This design cycle is for planning such feature on convenient usage as holding in one hand while locking an object*

*Functions are weighed by proportional values. Each design cycle shows status of a specific function implementation. Zero means function is planned but not implemented, empty cell means function is not*

*planned, and the presence of a numerical value shows implementation.*

with 1 (100%).

*Task-Based Conceptual Design of a Novel Product DOI: http://dx.doi.org/10.5772/intechopen.92155*

**Figure 10.**

**Figure 11.**

**11**

*and releasing the object upon necessity.*

## **2.4 Database modification and usage as a means for synthesizing action along with other synthesizing tools**

The model presented in **Figure 8** serves the formulation of the problem and setting objective of synthesis which should be searched or prepared, depending on its availability in database or by satisfying challenging function by an initiated movement of a link. In other words, the first action relates to the search and to the usage of a confirmed function from database, which in wider interpretation can be considered as known mechanisms or fragment of mechanisms, and for the second case, setting a movable link originated from the basic one in a way to provide one of the common mechanical primitive functions (cam, screw, lever, gear, etc. mechanisms). The second action relates to a donation of degree of freedom action, where the movement of the novel originated link relative to the basic link may be interpreted in a wider range of functions than normal, in which it is interpreted by the designer. It is worthy to emphasize that same modeling techniques for mechanism and function (**Figures 1** and **2**), modification (**Figures 5** and **6**), and modeling (**Figure 8**) combined as a task block of conceptual design are identically applicable for a solution means. Corresponding models (**Figures 1, 2, 5, 6** and **8**) are valid for solution block, and those models could be developed using the same abovedescribed procedure and modification techniques. Once a solution block is developed upon requirements of conceptual design, then this block could be aggregated with links of the preparation and task setting block. Graphically this aggregation is shown in **Figure 9** where two identical per-content and per-development procedure blocks stand on the left and right sides relative to centrally located aggregation symbols *AMD* (A for aggregation, M for mechanical block, D for database or solution block) which are representing edges of the graph, connecting corresponding links from task preparation block and solution block.

**Figure 9.** *Model for aggregation graph connecting task solution preparation block on the left and database or solution block on the right.*

## **3. Application task-based conceptual design method on analyses and reinventing of a locking pliers (US patent, 1970)**

## **3.1 Eight sets of design cycles**

Eight sets of design cycles below track the concept design or redesign of a known tool locking pliers patented in the USA in 1970. The process starts with

## *Task-Based Conceptual Design of a Novel Product DOI: http://dx.doi.org/10.5772/intechopen.92155*

setting of objectives for design and ends with the structural diagram. Each design cycle is provided by explanations cited in **Figures 10**–**17**. Each design cycle shows the degree of implementation of design goal starting from 0 and ending with 1 (100%).

## **Figure 10.**

to satisfy the sought function or may gain this ability after proper modification,

Localization of *mechanical and functional* fragments relates not only to neighbor components as it stated for convenience and simplicity but to any combination of

**2.4 Database modification and usage as a means for synthesizing action along**

The model presented in **Figure 8** serves the formulation of the problem and setting objective of synthesis which should be searched or prepared, depending on its availability in database or by satisfying challenging function by an initiated movement of a link. In other words, the first action relates to the search and to the usage of a confirmed function from database, which in wider interpretation can be considered as known mechanisms or fragment of mechanisms, and for the second case, setting a movable link originated from the basic one in a way to provide one of the common mechanical primitive functions (cam, screw, lever, gear, etc. mechanisms). The second action relates to a donation of degree of freedom action, where

the movement of the novel originated link relative to the basic link may be

interpreted in a wider range of functions than normal, in which it is interpreted by the designer. It is worthy to emphasize that same modeling techniques for mechanism and function (**Figures 1** and **2**), modification (**Figures 5** and **6**), and modeling (**Figure 8**) combined as a task block of conceptual design are identically applicable for a solution means. Corresponding models (**Figures 1, 2, 5, 6** and **8**) are valid for solution block, and those models could be developed using the same abovedescribed procedure and modification techniques. Once a solution block is developed upon requirements of conceptual design, then this block could be aggregated with links of the preparation and task setting block. Graphically this aggregation is shown in **Figure 9** where two identical per-content and per-development procedure blocks stand on the left and right sides relative to centrally located aggregation symbols *AMD* (A for aggregation, M for mechanical block, D for database or solution block) which are representing edges of the graph, connecting corresponding links from task preparation block and solution block.

**3. Application task-based conceptual design method on analyses and**

*Model for aggregation graph connecting task solution preparation block on the left and database or solution*

Eight sets of design cycles below track the concept design or redesign of a known tool locking pliers patented in the USA in 1970. The process starts with

**reinventing of a locking pliers (US patent, 1970)**

**3.1 Eight sets of design cycles**

**Figure 9.**

**10**

*block on the right.*

adding links, chains, grouping links, etc.

components from **Figures 5** and **6**.

*Product Design*

**with other synthesizing tools**

*This is a task for synthesis; four basic functions are set as subject for implementation. Lock an object, provide adjustable thickness of locked object, develop necessary pressure for locking, and release the object if necessary. Functions are weighed by proportional values. Each design cycle shows status of a specific function implementation. Zero means function is planned but not implemented, empty cell means function is not planned, and the presence of a numerical value shows implementation.*

## **Figure 11.**

*This design cycle is for planning such feature on convenient usage as holding in one hand while locking an object and releasing the object upon necessity.*

## **Figure 12.**

*Adjustable feature as well the object locking feature are provided by pivoting Jaw1 against Jaw2, an action which can be explained also by an action of granting DOF.*

## **Figure 13.**

*Property of developing pressure is implemented by an action of inserting a fragment of four bar mechanism, which at the region of end point is able to generate high force by lower amount of movement; a special virtual mechanism is developed (b, c) for explanation and confirmation of high transmission ratio feature.*

purpose hand tool in the market, and serves as a frequently used tool for a house-

*Development of virtual mechanism for explaining the locking-fixing function of locking pliers. The virtual mechanisms provides confirmation and positive usage of locking force and neutralization of a force that*

*attempts to release and cancel locked condition of locking pliers.*

*Task-Based Conceptual Design of a Novel Product DOI: http://dx.doi.org/10.5772/intechopen.92155*

The original stage of design includes situation when one of the main features of one hand operation should be revealed and described by virtual mechanisms. For that reason, the palm and three fingers of human hand namely little finger, index

Two features of this new creative hand tool are planned and structurally implemented. Novel features of self-adjustable nut wrench are quick- and selfadjustment and one-hand operation. The disadvantage is lower level of torque ability. The adjustable nut driver (known product) has slower adjustment action, high torque, and the necessity of two-hand operation for adjustment of the distance between the jaws. It has no self-adjustment feature and human control is needed for

*A slider is provided for connecting rod, allowing to achieve the adjustable feature of locking pliers.*

hold user. A comparison list of two products is shown in **Table 1**.

adjustment.

**13**

**Figure 15.**

**Figure 14.**

## **4. Conceptual design example for a self-adjustable nut wrench**

## **4.1 Task of conceptual design**

The presented example of conceptual design refers to the search and finding a solution for a unique structure hand tool—a self and fast action adjustable socket nut wrench [10]. According to its features, it replaces a large socket set for both metric and English sizes, differs in its features and outer look from any similar

*Task-Based Conceptual Design of a Novel Product DOI: http://dx.doi.org/10.5772/intechopen.92155*

## **Figure 14.**

*Development of virtual mechanism for explaining the locking-fixing function of locking pliers. The virtual mechanisms provides confirmation and positive usage of locking force and neutralization of a force that attempts to release and cancel locked condition of locking pliers.*

## **Figure 15.**

*A slider is provided for connecting rod, allowing to achieve the adjustable feature of locking pliers.*

purpose hand tool in the market, and serves as a frequently used tool for a household user. A comparison list of two products is shown in **Table 1**.

Two features of this new creative hand tool are planned and structurally implemented. Novel features of self-adjustable nut wrench are quick- and selfadjustment and one-hand operation. The disadvantage is lower level of torque ability. The adjustable nut driver (known product) has slower adjustment action, high torque, and the necessity of two-hand operation for adjustment of the distance between the jaws. It has no self-adjustment feature and human control is needed for adjustment.

The original stage of design includes situation when one of the main features of one hand operation should be revealed and described by virtual mechanisms. For that reason, the palm and three fingers of human hand namely little finger, index

**4. Conceptual design example for a self-adjustable nut wrench**

*Property of developing pressure is implemented by an action of inserting a fragment of four bar mechanism, which at the region of end point is able to generate high force by lower amount of movement; a special virtual mechanism is developed (b, c) for explanation and confirmation of high transmission ratio feature.*

*Adjustable feature as well the object locking feature are provided by pivoting Jaw1 against Jaw2, an action*

*which can be explained also by an action of granting DOF.*

The presented example of conceptual design refers to the search and finding a solution for a unique structure hand tool—a self and fast action adjustable socket nut wrench [10]. According to its features, it replaces a large socket set for both metric and English sizes, differs in its features and outer look from any similar

**4.1 Task of conceptual design**

**Figure 12.**

*Product Design*

**Figure 13.**

**12**

## **Figure 16.**

*A new link L13 is arranged between the lower handle and connecting rod to allow the mechanism to be released from a locked state when the release button is triggered.*

screw or the nut head that leads to the accomplishment of ratcheting function. Implementation of the ratcheting feature is missing in the current set of design cycles. Detailed implementation inside each set of functions is based on building of an open chain or elementary set of sliders and rotational links denoting specific functional meaning to each of them and then continued by attempts to accomplish those movements by the synthesis tools of grating degrees of freedom, by freezing

**Adjustable nut driver: known product Self-adjustable nut wrench: new product**

Two-hand usage One-hand usage Not so convenient Convenient Adjustable Adjustable Slow adjustable Quick adjustable Heavy duty Light duty Ratcheting Ratcheting Higher torque Lower torque

*Task-Based Conceptual Design of a Novel Product DOI: http://dx.doi.org/10.5772/intechopen.92155*

Not convenient to release Convenient to release Not easy ratchet adjustment Easy ratchet adjustment Range of adjustment: wide Range of adjustment: narrow

Two-hand adjustment is needed Self-adjustable

of movements, duplicating by parallel chains, and conditioning by drives.

are listed. Explanations for each step are cited in **Figures 18**–**27**.

**wrench**

**Figure 18.**

**15**

**Table 1.**

*Feature list of existing and new nut wrenches.*

**4.2 Ten sets of design cycles for conceptual design of self-adjustable nut**

Below 10 steps of conceptual design for the novel tool per predefined features

*The first design cycle is setting objectives of design, listed red highlighted in the section state of function. The links*

*involved in the design are connected in a way to plan implementation of required functions.*

## **Figure 17.**

*This design cycle is revealing the inconvenience of using the locking pliers for release function; so far two hands are needed for this operation.*

finger and palm of human hand are described by granting one-degree movements duplicated by drive chains each of them attached to the future handle of future tool to implement four functions: holding, adjustment, screwing, and releasing operations by a single hand. So the model at this stage of conceptual design includes human hand, handle of the tool, nut, bolt, ground floor, human feet, human body, and the chain, which is closed at the already mentioned human hand.

The next set of functions lying in the model for action synthesis of the mechanism responsible for fast, not reversible, and secure adjustment holding the nut head during screwing operation needs to be inserted in a block located between the first set of functions. The third set of featured relates to the portioned rotation of


## **Table 1.**

*Feature list of existing and new nut wrenches.*

screw or the nut head that leads to the accomplishment of ratcheting function. Implementation of the ratcheting feature is missing in the current set of design cycles. Detailed implementation inside each set of functions is based on building of an open chain or elementary set of sliders and rotational links denoting specific functional meaning to each of them and then continued by attempts to accomplish those movements by the synthesis tools of grating degrees of freedom, by freezing of movements, duplicating by parallel chains, and conditioning by drives.

## **4.2 Ten sets of design cycles for conceptual design of self-adjustable nut wrench**

Below 10 steps of conceptual design for the novel tool per predefined features are listed. Explanations for each step are cited in **Figures 18**–**27**.

## **Figure 18.**

*The first design cycle is setting objectives of design, listed red highlighted in the section state of function. The links involved in the design are connected in a way to plan implementation of required functions.*

finger and palm of human hand are described by granting one-degree movements duplicated by drive chains each of them attached to the future handle of future tool to implement four functions: holding, adjustment, screwing, and releasing operations by a single hand. So the model at this stage of conceptual design includes human hand, handle of the tool, nut, bolt, ground floor, human feet, human body,

*This design cycle is revealing the inconvenience of using the locking pliers for release function; so far two hands*

*A new link L13 is arranged between the lower handle and connecting rod to allow the mechanism to be released*

The next set of functions lying in the model for action synthesis of the mechanism responsible for fast, not reversible, and secure adjustment holding the nut head during screwing operation needs to be inserted in a block located between the first set of functions. The third set of featured relates to the portioned rotation of

and the chain, which is closed at the already mentioned human hand.

**Figure 16.**

*Product Design*

**Figure 17.**

**14**

*are needed for this operation.*

*from a locked state when the release button is triggered.*

## **Figure 19.**

*The second design cycle is for modeling nut rotation operation, including necessary functions of nut rotation and axial sliding because of screw movement.*

## **Figure 20.**

*The third design cycle serves for building a virtual mechanism, providing convenience of usage due to holding of the tool in one hand and providing full control during nut tightening and releasing operations.*

2.Mechanical diagrams are broken down and equipped with virtual mechanisms for detailed revelation of hidden functions for a more comprehensive consideration of functions and finding ways for their satisfaction.

*The feature of adjustable size for a nut is planned in this design cycle. Jaws are separated from the socket base to allow adjustable feature. Still unknown connection between the jaws and socket body is symbolized by parallel*

*The fourth design cycle is for providing convenient usage of the tool when adjusting opening between the jaws by pressing on the tool by right hand thumb while holding the body of toll by palm and two fingers of the same right hand.*

3.Mechanical and functional graphs are built in a way to express both structural contents of mechanism and functions subject to implementation in both the

planning state and implementation state.

*lines urging to touch each other and belonging to neighbor links.*

*Task-Based Conceptual Design of a Novel Product DOI: http://dx.doi.org/10.5772/intechopen.92155*

**Figure 21.**

**Figure 22.**

**17**

## **5. Conclusions**

1.The study is devoted to the development of a more visualized environment for conceptual design, based on mechanical diagrams, mechanical-functional graphs and models, and concept design result evaluation based on weighted functions.

*Task-Based Conceptual Design of a Novel Product DOI: http://dx.doi.org/10.5772/intechopen.92155*

## **Figure 21.**

*The fourth design cycle is for providing convenient usage of the tool when adjusting opening between the jaws by pressing on the tool by right hand thumb while holding the body of toll by palm and two fingers of the same right hand.*

### **Figure 22.**

*The feature of adjustable size for a nut is planned in this design cycle. Jaws are separated from the socket base to allow adjustable feature. Still unknown connection between the jaws and socket body is symbolized by parallel lines urging to touch each other and belonging to neighbor links.*


**5. Conclusions**

**Figure 20.**

**16**

**Figure 19.**

*Product Design*

*axial sliding because of screw movement.*

1.The study is devoted to the development of a more visualized environment for conceptual design, based on mechanical diagrams, mechanical-functional graphs and models, and concept design result evaluation based on weighted functions.

*The third design cycle serves for building a virtual mechanism, providing convenience of usage due to holding of*

*the tool in one hand and providing full control during nut tightening and releasing operations.*

*The second design cycle is for modeling nut rotation operation, including necessary functions of nut rotation and*

## **Figure 23.**

*The design cycle shows an implementation step of planned function (adjustable). Jaw L12 is linked rotationally to the socket base L3. The graph shows this rotational connection by R312 symbol continued from symbol of planned function F3.*

**Figure 25.**

*Task-Based Conceptual Design of a Novel Product DOI: http://dx.doi.org/10.5772/intechopen.92155*

*F17, and F18.*

**Figure 26.**

**19**

*control over jaw opening.*

*At this design step, the four functions planned previously (design cycle 7) are implemented. Physical kinematical joints marked in square symbols are connecting links L3, L12, and L13 to provide required functions F15, F16,*

*The design cycle provides necessary connections between human hand finger socket base and sleeve for convenient*

## **Figure 24.**

*The mechanical set is expanded by means of a sleeve L13 being in proper connection with the socket base and jaw which provides necessary functions of locking a nut, irreversible lock, self-adjustable feature, and still keeping the major function of the adjustable. This design function is a planning step for red highlighted functions.*

*Task-Based Conceptual Design of a Novel Product DOI: http://dx.doi.org/10.5772/intechopen.92155*

### **Figure 25.**

**Figure 23.**

*Product Design*

**Figure 24.**

**18**

*planned function F3.*

*The design cycle shows an implementation step of planned function (adjustable). Jaw L12 is linked rotationally to the socket base L3. The graph shows this rotational connection by R312 symbol continued from symbol of*

*The mechanical set is expanded by means of a sleeve L13 being in proper connection with the socket base and jaw which provides necessary functions of locking a nut, irreversible lock, self-adjustable feature, and still keeping the major function of the adjustable. This design function is a planning step for red highlighted functions.*

*At this design step, the four functions planned previously (design cycle 7) are implemented. Physical kinematical joints marked in square symbols are connecting links L3, L12, and L13 to provide required functions F15, F16, F17, and F18.*

## **Figure 26.**

*The design cycle provides necessary connections between human hand finger socket base and sleeve for convenient control over jaw opening.*

## **Figure 27.**

*Physical implementation of functions planned in design cycle 9 by duplicating functional edges of the graph with cam, rotational, and prismatic kinematical joints.*


**Author details**

Hrayr Darbinyan

**21**

Shanghai Kunjek Handtools and Hardware Co. Ltd., Shanghai, China

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to: hvdarbin@yahoo.com

provided the original work is properly cited.

*Task-Based Conceptual Design of a Novel Product DOI: http://dx.doi.org/10.5772/intechopen.92155*

*Task-Based Conceptual Design of a Novel Product DOI: http://dx.doi.org/10.5772/intechopen.92155*

## **Author details**

4.An exemplary set of design cycles is presented for serving needs of reverse engineering or reinventing of a known product, demonstrating, analyzing, and

*Physical implementation of functions planned in design cycle 9 by duplicating functional edges of the graph with*

5.A hierarchical set of the traditional presentation of functions and its evaluation is upgraded into a matrix formatted table, allowing to track step-by-step implementation of tasks, still keeping hierarchical relations and numerical evaluation of conceptual design tasks at each step of the design cycle.

6.The same visualized approach is applied for structural synthesis of a novel product based on novel advantageous features and based on property

comparison with a competitive product.

*cam, rotational, and prismatic kinematical joints.*

**Figure 27.**

*Product Design*

**20**

explaining possibilities of the method applied to a known product.

Hrayr Darbinyan Shanghai Kunjek Handtools and Hardware Co. Ltd., Shanghai, China

\*Address all correspondence to: hvdarbin@yahoo.com

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## **References**

[1] Chiou S-J, Kota S. Automated conceptual design of mechanisms. Mechanism and Machine Theory. 1999; **34**:467-496

[2] Paredis CJJ, Khosla PK. Synthesis methodology for task based reconfiguration of modular manipulator systems. In: Proceedings of 6th International Sumposium on Robotic Research, Hidden Valley, PA. 1993

[3] Arlitt R, Van Bossuyt DL, Stone RB, Tumer IY. The function-based design for sustainability method. Journal of Mechanical Design. 2017;**139**: 041102-041103

[4] Freudenstein F, Maki ER. The creation of mechanisms according to kinematic structure and function. Environment and Planning B. 1979;**6**: 375-391

[5] Hong-Sen Y. A methodology for creative mechanism design. Mechanism and Machine Theory. 1992;**27**(3): 235-242

[6] Pahl G, Beitz W. Engineering Design, A Systematic Approach. NY: Springer; 1995

[7] Altshuller GS. Creativity as an exact science. In: Theory of Solving Inventive Problems. Moscow: Sov. Radio, Cybernetics; 1979

[8] Moehrle MG. How Combinations of TRIZ Tools are Used in Companies-Result of a Cluster Analyses. Blockwell Publishing Ltd, R&D Management; 2005;**35**(3):285-296

[9] Darbinyan HV. Task based conceptual design method. In: Proceedings of 13th World Congress in Mechanism and Machine Science. Mexico: Guanajuato; 2011. pp. A23-A559 [10] Darbinyan HV. US Patent 8,485,070. T-Handle Wrench. 2013

**23**

**Chapter 2**

**Abstract**

*Rosnani Ginting*

design, to production.

**1. Introduction**

Design Methods

Integrated Model of Product

A critical factor in product innovation creativity is the development of design methodologies in various fields. The design and manufacture of a product, whether new or existing, is a significant part of engineering activities. The ability to design, develop, and produce products that customers want efficiently is the key to success in today's dynamic global market. Among these capabilities is the ability to design products that are competitive, cost-effective, and ready to be marketed on time. One key factor for maintaining competitiveness in the market is the focus on product and innovation processes by using various integrated design methods that are implemented as a standard part of design activities. The innovative integrated method, which combines various product design methods precisely can solve the main contradictory problems in the process from product demand analysis, product

**Keywords:** product design, integrated model, quality function deployment (QFD)

optimum level of the product quality based on customers' needs [2].

cost can be used to create a new alternative of product design.

In the actual market situation, manufacturing companies must develop products that can be accepted by the customer, and at the same time, this product must be able to give satisfaction to the customer. Product design must be optimized by considering the costs, design requirements, and customers' needs [1]. The economic success of a manufacturing company depends on the ability to identify customer needs, then create products that meet those needs at a low cost. The company strategy is strongly related to the design and production processes with the most

Various priorities can be increased competition in the company: quality, the speed of delivery, cost, innovation, and product limitations [3]. The customers' satisfaction and optimizing the total value of the product design is an essential goal for product development time. After defining the design of the product, the production

Many companies have tried various new approaches in product design to stay competitive. With globalization, enterprises have to compete with both local and international companies. Many methods and techniques are used by some manufacturing companies to enhance product competitiveness by fulfilling customer desire and satisfaction by improving the quality of product design. Many researchers suggest a variety of design tools that were implemented early in the design process. Therefore, various design techniques have already been developed, generated,

## **Chapter 2**

**References**

*Product Design*

**34**:467-496

041102-041103

375-391

235-242

1995

**22**

[1] Chiou S-J, Kota S. Automated conceptual design of mechanisms. Mechanism and Machine Theory. 1999; [10] Darbinyan HV. US

2013

Patent 8,485,070. T-Handle Wrench.

[2] Paredis CJJ, Khosla PK. Synthesis

reconfiguration of modular manipulator

[3] Arlitt R, Van Bossuyt DL, Stone RB, Tumer IY. The function-based design for sustainability method. Journal of Mechanical Design. 2017;**139**:

[4] Freudenstein F, Maki ER. The creation of mechanisms according to kinematic structure and function. Environment and Planning B. 1979;**6**:

[5] Hong-Sen Y. A methodology for creative mechanism design. Mechanism and Machine Theory. 1992;**27**(3):

[6] Pahl G, Beitz W. Engineering Design, A Systematic Approach. NY: Springer;

[7] Altshuller GS. Creativity as an exact science. In: Theory of Solving Inventive

[8] Moehrle MG. How Combinations of TRIZ Tools are Used in Companies-Result of a Cluster Analyses. Blockwell Publishing Ltd, R&D Management;

Proceedings of 13th World Congress in Mechanism and Machine Science. Mexico: Guanajuato; 2011. pp. A23-A559

Problems. Moscow: Sov. Radio,

[9] Darbinyan HV. Task based conceptual design method. In:

Cybernetics; 1979

2005;**35**(3):285-296

methodology for task based

systems. In: Proceedings of 6th International Sumposium on Robotic Research, Hidden Valley, PA. 1993

## Integrated Model of Product Design Methods

*Rosnani Ginting*

## **Abstract**

A critical factor in product innovation creativity is the development of design methodologies in various fields. The design and manufacture of a product, whether new or existing, is a significant part of engineering activities. The ability to design, develop, and produce products that customers want efficiently is the key to success in today's dynamic global market. Among these capabilities is the ability to design products that are competitive, cost-effective, and ready to be marketed on time. One key factor for maintaining competitiveness in the market is the focus on product and innovation processes by using various integrated design methods that are implemented as a standard part of design activities. The innovative integrated method, which combines various product design methods precisely can solve the main contradictory problems in the process from product demand analysis, product design, to production.

**Keywords:** product design, integrated model, quality function deployment (QFD)

## **1. Introduction**

In the actual market situation, manufacturing companies must develop products that can be accepted by the customer, and at the same time, this product must be able to give satisfaction to the customer. Product design must be optimized by considering the costs, design requirements, and customers' needs [1]. The economic success of a manufacturing company depends on the ability to identify customer needs, then create products that meet those needs at a low cost. The company strategy is strongly related to the design and production processes with the most optimum level of the product quality based on customers' needs [2].

Various priorities can be increased competition in the company: quality, the speed of delivery, cost, innovation, and product limitations [3]. The customers' satisfaction and optimizing the total value of the product design is an essential goal for product development time. After defining the design of the product, the production cost can be used to create a new alternative of product design.

Many companies have tried various new approaches in product design to stay competitive. With globalization, enterprises have to compete with both local and international companies. Many methods and techniques are used by some manufacturing companies to enhance product competitiveness by fulfilling customer desire and satisfaction by improving the quality of product design. Many researchers suggest a variety of design tools that were implemented early in the design process. Therefore, various design techniques have already been developed, generated,

and some of them are implemented as a design activity in some manufactures [4]. Various methods have been developed to help collect, organize, analyze, synthesize, and display the information used in the design process [5]. According to Sakao [4], various design guidelines have been developed, while a large number of individual design methods and tools have been generated, of which some were implemented as a standard part of design activities.

## **2. Design on engineering perspective**

Design based on an engineering perspective is the application of scientific, mathematical, and creative concepts that are imagined into structures, machines, and systems that display the functions of an engineering perspective. In the process of designing consumer products in addition to the form and function of the product, engineering and industrial design are very important in the development of these products depends on the engineer and industrial designer, where the engineer functions as a determinant of the product function and the industrial designer functions to add aesthetic value in the design.

The company's ability to design, develop, and produce products that customers want efficiently is the key to success in today's dynamic global market. Among these capabilities is the ability to design products that meet the demands of competitive and cost-effective products and are ready to be marketed on time. So, companies need to develop strategic goals based on achievement, which creates a competitive advantage in the market. However, these efforts often have limitations in establishing a systematic and consistent set of methods.

## **3. Product design & development**

Product design is the process of developing practical and effective ideas for producing new products, encompassing all the engineering and industrial design work used to develop products, from initial concept to production [6]. In this phase, important decisions are made that affect other activities. The general product development goal has not changed much over time: design a product that sale lots of the right margin. Another way to say this is: design the right product the first time, while designing the product right the first time. Product design has been widely studied in order to create methodologies that are generic enough to develop new products. The systematic method developed by Ulrich and Eppinger [7] structures the product development process according to four stages (see **Figure 1**).

This product-oriented approach defines the design process as a sequence of different phases. The transition from the task to the solution takes place in a succession of different stages. Many academic practitioners and researchers have proposed many design principles and methods to improve the quality of design, and some design methods are implemented as part of the design activities of some manufacturing companies. Each of these phases makes it possible to detail a result. Thus the

**25**

requirements.

*Integrated Model of Product Design Methods DOI: http://dx.doi.org/10.5772/intechopen.92059*

**4. Integrated of product design methodology**

establishing a systematic and consistent set of methods.

and how it can be modified to be more productive.

**4.1 Quality function deployment method**

and methodologies commonly used in designing a product [17].

documents.

specifications of the design problem are determined when the issue is clarified and defined. These specifications are then used during the various stages of the development process. Then comes the definition of the structure of the functions, the principles of solution, the structural model, technical plans and then production

The ability to quickly identify the required method is an essential priority in product achievement and design process [8]. Any company that meets the specifications and requirements of the customer will usually be more competitive than the others [9]. The ability of a company to efficiently design, develop, and manufacture customers' favorite products is key to its success in today's dynamic global marketplace. Among these capabilities is the ability to design products that meet customer needs at a competitive cost and are ready to be marketed promptly [10]. So, companies need to develop strategic goals based on achievements that create a competitive advantage in the market [11]. However, these efforts often have limitations in

There are many cases where it is difficult to find a product by merely relying on today's technology, such as technical innovation, and the identification of available technological collections in other sectors or areas will be a crucial factor. Therefore, a company must be able to innovate to meet customer needs [12]. A key factor in product innovation creativity methods is the development of design methodology methodologies that have been developed for product development [10, 13–15]. Lance and Bonollo [16] argued that the design method was about procedures, engineering, and design process. The development of design methodology includes research on design principles, practices, and procedures. The main focus on developing a product requires a deep and practical understanding of the design process

Many academic researchers have proposed various principles of design ethics to improve the quality of design, some of which have been implemented as part of design activities in some manufacturing companies [4]. As a paradigm for simultaneous engineering design processes, it is possible to adopt various design theories

QFD has been recognized as an effective method for integrated products. QFD is a structured approach for integrating customer voice into product design and development [18]. The introduction of the QFD into the Americas and the European Region began in 1983 and today, and QFD continues to provide strong inspiration worldwide in the academic and manufacturing world. It is widely used in many industries such as the automotive, electronics, construction, and services sectors [19]. QFD is a multifaceted process, offering the greatest potential for significant benefits [20]. QFD is recognized as an effective method for the development of integrated processes and products [21]. The QFD aims to increase customer satisfaction based on their needs and also to enhance the profitability of the company [22]. In other words, QFD is a way of transforming the customers' desire into product design [23]. Further, Lai et al. [24], stated that QFD is a general concept that provides a means for translating customer requirements into technical

**Figure 1.** *Product development process [7].* *Product Design*

a standard part of design activities.

**2. Design on engineering perspective**

functions to add aesthetic value in the design.

ing a systematic and consistent set of methods.

**3. Product design & development**

and some of them are implemented as a design activity in some manufactures [4]. Various methods have been developed to help collect, organize, analyze, synthesize, and display the information used in the design process [5]. According to Sakao [4], various design guidelines have been developed, while a large number of individual design methods and tools have been generated, of which some were implemented as

Design based on an engineering perspective is the application of scientific, mathematical, and creative concepts that are imagined into structures, machines, and systems that display the functions of an engineering perspective. In the process of designing consumer products in addition to the form and function of the product, engineering and industrial design are very important in the development of these products depends on the engineer and industrial designer, where the engineer functions as a determinant of the product function and the industrial designer

The company's ability to design, develop, and produce products that customers want efficiently is the key to success in today's dynamic global market. Among these capabilities is the ability to design products that meet the demands of competitive and cost-effective products and are ready to be marketed on time. So, companies need to develop strategic goals based on achievement, which creates a competitive advantage in the market. However, these efforts often have limitations in establish-

Product design is the process of developing practical and effective ideas for producing new products, encompassing all the engineering and industrial design work used to develop products, from initial concept to production [6]. In this phase, important decisions are made that affect other activities. The general product development goal has not changed much over time: design a product that sale lots of the right margin. Another way to say this is: design the right product the first time, while designing the product right the first time. Product design has been widely studied in order to create methodologies that are generic enough to develop new products. The systematic method developed by Ulrich and Eppinger [7] structures

the product development process according to four stages (see **Figure 1**).

This product-oriented approach defines the design process as a sequence of different phases. The transition from the task to the solution takes place in a succession of different stages. Many academic practitioners and researchers have proposed many design principles and methods to improve the quality of design, and some design methods are implemented as part of the design activities of some manufacturing companies. Each of these phases makes it possible to detail a result. Thus the

**24**

**Figure 1.**

*Product development process [7].*

specifications of the design problem are determined when the issue is clarified and defined. These specifications are then used during the various stages of the development process. Then comes the definition of the structure of the functions, the principles of solution, the structural model, technical plans and then production documents.

## **4. Integrated of product design methodology**

The ability to quickly identify the required method is an essential priority in product achievement and design process [8]. Any company that meets the specifications and requirements of the customer will usually be more competitive than the others [9]. The ability of a company to efficiently design, develop, and manufacture customers' favorite products is key to its success in today's dynamic global marketplace. Among these capabilities is the ability to design products that meet customer needs at a competitive cost and are ready to be marketed promptly [10]. So, companies need to develop strategic goals based on achievements that create a competitive advantage in the market [11]. However, these efforts often have limitations in establishing a systematic and consistent set of methods.

There are many cases where it is difficult to find a product by merely relying on today's technology, such as technical innovation, and the identification of available technological collections in other sectors or areas will be a crucial factor. Therefore, a company must be able to innovate to meet customer needs [12]. A key factor in product innovation creativity methods is the development of design methodology methodologies that have been developed for product development [10, 13–15].

Lance and Bonollo [16] argued that the design method was about procedures, engineering, and design process. The development of design methodology includes research on design principles, practices, and procedures. The main focus on developing a product requires a deep and practical understanding of the design process and how it can be modified to be more productive.

Many academic researchers have proposed various principles of design ethics to improve the quality of design, some of which have been implemented as part of design activities in some manufacturing companies [4]. As a paradigm for simultaneous engineering design processes, it is possible to adopt various design theories and methodologies commonly used in designing a product [17].

## **4.1 Quality function deployment method**

QFD has been recognized as an effective method for integrated products. QFD is a structured approach for integrating customer voice into product design and development [18]. The introduction of the QFD into the Americas and the European Region began in 1983 and today, and QFD continues to provide strong inspiration worldwide in the academic and manufacturing world. It is widely used in many industries such as the automotive, electronics, construction, and services sectors [19]. QFD is a multifaceted process, offering the greatest potential for significant benefits [20]. QFD is recognized as an effective method for the development of integrated processes and products [21]. The QFD aims to increase customer satisfaction based on their needs and also to enhance the profitability of the company [22]. In other words, QFD is a way of transforming the customers' desire into product design [23]. Further, Lai et al. [24], stated that QFD is a general concept that provides a means for translating customer requirements into technical requirements.

QFD is a systematic approach that determines consumer demands or requests and then translates these demands accurately into technical, manufacturing, and appropriate production planning. Revelle [25] argued that QFD was created to help an organization improve its ability to understand its customers' needs as well as to respond to those needs effectively. It means that QFD is created to help organizations improve the organizational capacity to understand customer needs, and respond effectively. QFD method is used because it can identify the customer needs and provide solutions to the existing problems. QFD described by house of quality contributes to the company about the attributes that need to be prioritized, improved and meet the customer needs.

Bouchereau and Rowlands [26], argued that the starting point of the QFD is customer preference, though often cited but measurable. These requirements will then be converted to technical specifications. Each phase of the QFD matrix represents a more specific aspect. However, only one of the essential aspects is moved into the next matrix.

Various names know the QFD matrix; the most common is the quality house (HoQ ). HoQ introduces cross-linking between customer needs and design change and between the design variants themselves. Each customers' requirement is converted into one or more technical specification at all levels of the structured project with interrelated matrix [24, 27, 28] (see **Figure 2**).

QFD is a method for developing design quality that aims at customer satisfaction and then translates these needs into design goals and quality assurance points to be used at all stages of production. QFD has been recognized as an effective method for integrated products. QFD is a structured approach to integrate customer voices into product design and development [18]. QFD continues to provide strong inspiration in the academic and manufacturing worlds [19]. QFD is recognized as an effective guide to the development of integrated processes and products [21]. The objective of QFD is to increase customer satisfaction of product fulfillment requirements and to increase the company's profit [22].

In other words, QFD is the method to change the customers' needs in product design [23]. Furthermore, Lai et al. [24] argued that QFD is a general concept that provides methods for translating customer needs into technical specifications. QFD is implemented as a multi-phase process, offering the greatest potential to realize significant benefits [20]. Bouchereau and Rowlands [26], also argued that the starting point of QFD is the customers' wishes, although often referred to but measurable. These needs will then be changed to the technical specification. Each QFD matrix phase represents a more specific aspect. However, only one of the essential aspect is deployed into the next matrix.

**27**

*Integrated Model of Product Design Methods DOI: http://dx.doi.org/10.5772/intechopen.92059*

claim 20–50% less.

**4.2 QFD: it's advantages and drawbacks**

and critical parts when conducting analysis.

production, and application.

**4.3 An integrated model of QFD and TRIZ for product design**

Although QFD has many advantages, there are still some general implementation problems in QFD. Many investigators have shown that the first phase and the second phase of QFD have many specific limitations and need to be combined with other technical norms. Kazemzadeh and Behzadian [42], have analyzed 650 articles about QFD and grouped them into four broad categories, namely general introduction, functional areas, refinery applications, and literature development.

Generally, QFD facilitates the organization in (1) understanding the needs, (2) prioritizing customer needs, (3) communicating between team experts to ensure decision making and reducing data loss, (4) designing a product that meets or exceeding customer requirements, and (5) strategic product. Hales and Staley [30] stated that using QFD can produce better product development at a cost paid by the customer. Also, based on its customer in different company, the benefits and the advantages of some of the research done, such as customer satisfaction and reduced product production time [31], improved communication through teamwork [32] and better design [33]. Also, Bicknell in Chan and Wu [34] reported that significant benefits when QFD were used a 30–50% reduction in engineering change, a 30–50% shorter design cycle, 20–60% lower startup cost, and a security

Lai et al. [24] recognized that QFD has great benefits that can help companies provide a better product, enhance their competitiveness in the market, and increase

translate customers' wishes as a goal for product specification. However, QFD is not always easy to implement, and some companies have problems using it, especially in large numbers, as well as complex systems. The problem is conventional QFD is not optimal because it's every stage of the process is subjective and qualitative during data collection to meet customer desires and to obtain technical specifications

On the other hand, the various problems faced at various stages of QFD implementation have been widely reported in the study, in particular the traditional QFD method [36, 37]. First, the methodological framework of the conventional QFD method is no longer suitable to meet the design and product development requirements [4, 34, 38, 39]. Second, The QFD matrix is too large, and Third, the time required for the matrix sequence deployment is too long, and the product time to be marketed is not acceptable [8]. Then fourth, QFD is difficult to meet the needs of different customer groups or segments [40]. Fifth, the customers' voice is still qualitative, cannot be measured, and often misleading, it is not systematic, and the terms of product function too complicated (in this case, engineering process) are not easily determined [36]. Sixth, the customers' requirement translated into engineering terms (technical specifications) obtained from the company is still vague, too subjective, difficult to verify, and expressed in the linguistic form [40, 41]. These problems or drawbacks prompted the need for other approaches to be added when applying the QFD method. There are many different methods for generating new ideas and selecting the ideas to create a new design or to improve existing ones. Combining QFD with other techniques helps to address these drawbacks and can form the basis of future research. The integrated innovation method, which combines QFD with another technique tool, can precisely solve main contradictory problems in the process from the product demand analysis to the product design,

customer satisfaction. Poel [35] showed that the main objective of QFD is to

**Figure 2.** *QFD Matrix [29].*

*Product Design*

next matrix.

improved and meet the customer needs.

with interrelated matrix [24, 27, 28] (see **Figure 2**).

ments and to increase the company's profit [22].

aspect is deployed into the next matrix.

QFD is a systematic approach that determines consumer demands or requests

Bouchereau and Rowlands [26], argued that the starting point of the QFD is customer preference, though often cited but measurable. These requirements will then be converted to technical specifications. Each phase of the QFD matrix represents a more specific aspect. However, only one of the essential aspects is moved into the

Various names know the QFD matrix; the most common is the quality house (HoQ ). HoQ introduces cross-linking between customer needs and design change and between the design variants themselves. Each customers' requirement is converted into one or more technical specification at all levels of the structured project

QFD is a method for developing design quality that aims at customer satisfaction and then translates these needs into design goals and quality assurance points to be used at all stages of production. QFD has been recognized as an effective method for integrated products. QFD is a structured approach to integrate customer voices into product design and development [18]. QFD continues to provide strong inspiration in the academic and manufacturing worlds [19]. QFD is recognized as an effective guide to the development of integrated processes and products [21]. The objective of QFD is to increase customer satisfaction of product fulfillment require-

In other words, QFD is the method to change the customers' needs in product design [23]. Furthermore, Lai et al. [24] argued that QFD is a general concept that provides methods for translating customer needs into technical specifications. QFD is implemented as a multi-phase process, offering the greatest potential to realize significant benefits [20]. Bouchereau and Rowlands [26], also argued that the starting point of QFD is the customers' wishes, although often referred to but measurable. These needs will then be changed to the technical specification. Each QFD matrix phase represents a more specific aspect. However, only one of the essential

and then translates these demands accurately into technical, manufacturing, and appropriate production planning. Revelle [25] argued that QFD was created to help an organization improve its ability to understand its customers' needs as well as to respond to those needs effectively. It means that QFD is created to help organizations improve the organizational capacity to understand customer needs, and respond effectively. QFD method is used because it can identify the customer needs and provide solutions to the existing problems. QFD described by house of quality contributes to the company about the attributes that need to be prioritized,

**26**

**Figure 2.** *QFD Matrix [29].*

## **4.2 QFD: it's advantages and drawbacks**

Generally, QFD facilitates the organization in (1) understanding the needs, (2) prioritizing customer needs, (3) communicating between team experts to ensure decision making and reducing data loss, (4) designing a product that meets or exceeding customer requirements, and (5) strategic product. Hales and Staley [30] stated that using QFD can produce better product development at a cost paid by the customer. Also, based on its customer in different company, the benefits and the advantages of some of the research done, such as customer satisfaction and reduced product production time [31], improved communication through teamwork [32] and better design [33]. Also, Bicknell in Chan and Wu [34] reported that significant benefits when QFD were used a 30–50% reduction in engineering change, a 30–50% shorter design cycle, 20–60% lower startup cost, and a security claim 20–50% less.

Lai et al. [24] recognized that QFD has great benefits that can help companies provide a better product, enhance their competitiveness in the market, and increase customer satisfaction. Poel [35] showed that the main objective of QFD is to translate customers' wishes as a goal for product specification. However, QFD is not always easy to implement, and some companies have problems using it, especially in large numbers, as well as complex systems. The problem is conventional QFD is not optimal because it's every stage of the process is subjective and qualitative during data collection to meet customer desires and to obtain technical specifications and critical parts when conducting analysis.

On the other hand, the various problems faced at various stages of QFD implementation have been widely reported in the study, in particular the traditional QFD method [36, 37]. First, the methodological framework of the conventional QFD method is no longer suitable to meet the design and product development requirements [4, 34, 38, 39]. Second, The QFD matrix is too large, and Third, the time required for the matrix sequence deployment is too long, and the product time to be marketed is not acceptable [8]. Then fourth, QFD is difficult to meet the needs of different customer groups or segments [40]. Fifth, the customers' voice is still qualitative, cannot be measured, and often misleading, it is not systematic, and the terms of product function too complicated (in this case, engineering process) are not easily determined [36]. Sixth, the customers' requirement translated into engineering terms (technical specifications) obtained from the company is still vague, too subjective, difficult to verify, and expressed in the linguistic form [40, 41]. These problems or drawbacks prompted the need for other approaches to be added when applying the QFD method. There are many different methods for generating new ideas and selecting the ideas to create a new design or to improve existing ones. Combining QFD with other techniques helps to address these drawbacks and can form the basis of future research. The integrated innovation method, which combines QFD with another technique tool, can precisely solve main contradictory problems in the process from the product demand analysis to the product design, production, and application.

## **4.3 An integrated model of QFD and TRIZ for product design**

Although QFD has many advantages, there are still some general implementation problems in QFD. Many investigators have shown that the first phase and the second phase of QFD have many specific limitations and need to be combined with other technical norms. Kazemzadeh and Behzadian [42], have analyzed 650 articles about QFD and grouped them into four broad categories, namely general introduction, functional areas, refinery applications, and literature development. Their findings show that some of the limitations of QFD, which need to be combined with particular applications to break QFD restraints.

QFD not only deals with product functions but also quality specifications. QFD can be accomplished by considering the adverse effects and evaluating the repair options. The TRIZ methodology can support better designers to find improvement solutions. Therefore, it is used in conjunction with QFD, because TRIZ methods, based on integrated innovation methods, can be organized in many ways. An essential element of TRIZ is conflict [43]. The essential aspects of TRIZ are discrepancies, 40 principles of creation, matrices, and scientific implications [21]. Also, the design of discrepancy matrices is useful for detecting the adverse effects of technical specifications under other improvements [44].

The synergy achieved between the four phases of QFD and TRIZ is a powerful tool for enabling product development in improvement as it emphasizes error prevention practices [10]. The synergies achieved can detect issues such as characteristic conflicts in goal specification as well as negative interactions between product structure, materials, manufacturing processes, and production control specifications.

Many researchers have worked on QFD and TRIZ combination and deployed TRIZ to address QFD problems and shortcomings. For example, Wang et al. [44] identified contradictions within TRIZ by defining methods based on HOQ (House of Quality) in QFD. Various main parameters can be extracted and used to resolve conflicts and contradictions in QFD [45]. Regazzoni et al. [46] pointed out that taking an innovative, active, and prospective approach is much more effective than showing passive reactions in preventing product collapse during its initial designation stages. TRIZ instrument was implemented to resolve these conflicts by translating the technical requirements into 39 designation parameters.

In the contradiction matrix, ameliorating parameters in rows and deteriorating parameters are arranged in columns. As QFD reveals the "what's" of required operations, the TRIZ instrument determines the "how's" of the required procedures [5]. Sakao [4] presented TRIZ as a set of technology trends related more to quality control. The purpose is to help designers to become more efficient in making improvements changes to their designs. The designers need only to focus on more

**29**

**Table 1.**

*Integrated Model of Product Design Methods DOI: http://dx.doi.org/10.5772/intechopen.92059*

QFD and TRIZ as in **Figure 3**.

principles 40 and 39 parameters.

Claudon [48]

2004 Marsot and

2013 Farsijani and

2016 Patel and

Torabdaneh [5]

Deshpande [54]

*TRIZ method of solving technical conflicts.*

influential components to improve the quality aspect of a product. This is because QFD reveals "what" of the required operations, while TRIZ instrument determines "how" of the required operations. Farsijani et al. [5] addressed the combination of

The TRIZ method focuses on solving problems and constraints inherent in QFD. For example, Wang et al. [44] identified contradictions in TRIZ by defining quality-based home methods in QFD. Regazzoni et al. [46] showed that innovative approaches, more active and prospective. The correlation matrix at the top of the quality house is the key QFD integrated with TRIZ [47]. **Table 1** shows a list of some relevant literature on sectors and areas related to TRIZ integration with QFD [27–33]. As shown in **Table 1**, various previous studies have integrated the TRIZ model into the QFD process, mainly in defining the optimum technical specification priority in product design. Numerous previous studies have systematically integrated QFD with TRIZ and enabled effective and systematic technical innovation for new products. TRIZ was developed to help engineers find innovative solutions during the technical product development process. Some case studies show that the proposed model enables designers to find solutions that are simple, innovative, and customer-centric. Therefore, researchers conclude that TRIZ can help QFD quantitatively identify technical requirements and critical section with inventive

**Year References Variables Applied in**

technical specifications

technical specifications

technical specifications

technical specifications

technical specifications

product details and costs

Customer requirements and technical specifications

Customer requirements and technical specifications

technical specifications

technical specifications

2012 Yihong et al. [6] Product details Material design/construction

product changes

Customer requirements and technical specifications

Design of washer

Computer packaging design

Design a message change tool

Design of high-performance

Design a laptop computer

Design of biomedical equipment (stent tracheal)

Design of mobile health tools

Design of power transformers

Total performance excellence

Design of lego foam toys

Laptop computer design

Knife design

machines

design

2002 Yamashina et al. [14] Customer requirements and

2009 Rau and Fang [49] Technical specifications and

2010 David et al. [50] Customer requirements and

2010 Butdee [51] Customer requirements and

2011 Yeh et al. [10] Customer requirements and

2013 Melgoza et al. [52] Customer requirements and

2016 Dos Santos et al. [55] Customer requirements and

2016 Suzianti et al. [56] Customer requirements and

2013 Shihdan Chen [53] Product specifications,

**Figure 3.** *QFD and it's application [5].*

## *Integrated Model of Product Design Methods DOI: http://dx.doi.org/10.5772/intechopen.92059*

*Product Design*

specifications.

Their findings show that some of the limitations of QFD, which need to be com-

QFD not only deals with product functions but also quality specifications. QFD can be accomplished by considering the adverse effects and evaluating the repair options. The TRIZ methodology can support better designers to find improvement solutions. Therefore, it is used in conjunction with QFD, because TRIZ methods, based on integrated innovation methods, can be organized in many ways. An essential element of TRIZ is conflict [43]. The essential aspects of TRIZ are discrepancies, 40 principles of creation, matrices, and scientific implications [21]. Also, the design of discrepancy matrices is useful for detecting the adverse

The synergy achieved between the four phases of QFD and TRIZ is a powerful tool for enabling product development in improvement as it emphasizes error prevention practices [10]. The synergies achieved can detect issues such as characteristic conflicts in goal specification as well as negative interactions between product structure, materials, manufacturing processes, and production control

Many researchers have worked on QFD and TRIZ combination and deployed TRIZ to address QFD problems and shortcomings. For example, Wang et al. [44] identified contradictions within TRIZ by defining methods based on HOQ (House of Quality) in QFD. Various main parameters can be extracted and used to resolve conflicts and contradictions in QFD [45]. Regazzoni et al. [46] pointed out that taking an innovative, active, and prospective approach is much more effective than showing passive reactions in preventing product collapse during its initial designation stages. TRIZ instrument was implemented to resolve these conflicts by translat-

In the contradiction matrix, ameliorating parameters in rows and deteriorating parameters are arranged in columns. As QFD reveals the "what's" of required operations, the TRIZ instrument determines the "how's" of the required procedures [5]. Sakao [4] presented TRIZ as a set of technology trends related more to quality control. The purpose is to help designers to become more efficient in making improvements changes to their designs. The designers need only to focus on more

bined with particular applications to break QFD restraints.

effects of technical specifications under other improvements [44].

ing the technical requirements into 39 designation parameters.

**28**

**Figure 3.**

*QFD and it's application [5].*

influential components to improve the quality aspect of a product. This is because QFD reveals "what" of the required operations, while TRIZ instrument determines "how" of the required operations. Farsijani et al. [5] addressed the combination of QFD and TRIZ as in **Figure 3**.

The TRIZ method focuses on solving problems and constraints inherent in QFD. For example, Wang et al. [44] identified contradictions in TRIZ by defining quality-based home methods in QFD. Regazzoni et al. [46] showed that innovative approaches, more active and prospective. The correlation matrix at the top of the quality house is the key QFD integrated with TRIZ [47]. **Table 1** shows a list of some relevant literature on sectors and areas related to TRIZ integration with QFD [27–33].

As shown in **Table 1**, various previous studies have integrated the TRIZ model into the QFD process, mainly in defining the optimum technical specification priority in product design. Numerous previous studies have systematically integrated QFD with TRIZ and enabled effective and systematic technical innovation for new products. TRIZ was developed to help engineers find innovative solutions during the technical product development process. Some case studies show that the proposed model enables designers to find solutions that are simple, innovative, and customer-centric. Therefore, researchers conclude that TRIZ can help QFD quantitatively identify technical requirements and critical section with inventive principles 40 and 39 parameters.


## **Table 1.**

*TRIZ method of solving technical conflicts.*

## **4.4 A novel of QFD combined TRIZ methodology**

At this phase, the identification of technical specifications and important parts objectively uses the technique of Brainstorming. The next step is to design the proposed concept of an appropriate integration method in dealing with the times that occur in the QFD process of the first and second phases, especially in addressing the contradiction between the technical specification variable (phase one of QFD) and the critical part variable (phase two QFD). This integration built through a combination of the QFD framework with the TRIZ method in a systematic and more integrated manner. Stages of the proposed Integrated QFD (IQFD)-TRIZ methodology framework can be seen in **Figure 4**.

The proposed QFD-TRIZ integration methodology in **Figure 5** which is used to develop the QFD model combined with a more integrated TRIZ can be described in fourteen stages in the followings:


In this phase, customers need to identify product variables uses a questionnaire with a Likert scale.

5.Test the content validity and reliability of the questionnaire through the criterion related validity test, and test reliability with Alpha Cronbachs' coefficient test technique.

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*Integrated Model of Product Design Methods DOI: http://dx.doi.org/10.5772/intechopen.92059*

9,3,1,0

uses a questionnaire with weight 9,3,1,0

8.Define and define the planning matrix

analyzing weights 9,3,1,0

stages of the existing model as follows:

2.Engineer's voice matrix identification

4.Determine the phase relationship matrix

5.Determine the phase correlation matrix

method is done uses Interviews.

specifications uses the 5,4,3,2,1 scale.

9,3,1 scale.

1.Identification of customer requirements matrix

3.Identification of the interest phase of the customer change

This phase uses a questionnaire with the Likert scale.

15.Product design.

9.Define technical specification priority and targets

10.Design improvements to phase one QFD uses TRIZ methods

11.Determination of critical parts is done with the Alerting method

phase two. The calculation is done by analyzing 9,3,1,0 weight

14.Design improvements to QFD phase two use TRIZ methods

12.Establishing relationships among critical sections (correlation matrix) by

13.Determine the phase of the relationship between the Critical Divisions in

Meanwhile, the existing integration methodology of QFD-TRIZ model by Melgoza et al. [52], was used as the development of a Novel integration model developed in the study (see **Figure 5**). The QFD integration methodology steps with the TRIZ model have been selected to be compared with the Novel IQFD-TRIZ. The

In this phase, customer voice recognition to know what customers want in terms of product design is done through the product survey. The data are qualitative data.

In this phase, defining the technical specifications of the existing IQFD – TRIZ

In this phase, the relationship between customer requirements and technical

6.In this phase, the relationship between each technical specification uses the

6.Establish relationships among technical specifications (correlation matrix)

7.Determine the phase of the relationship between customer requirements and technical specifications, uses the survey, submitted to the company, weighing

**Figure 4.** *Novel Integration model of QFD-TRIZ.*

*Product Design*

**4.4 A novel of QFD combined TRIZ methodology**

methodology framework can be seen in **Figure 4**.

1.To determine and establish research objects.

information related to what customers complained.

fourteen stages in the followings:

identify customers' needs.

questionnaire.

with a Likert scale.

test technique.

At this phase, the identification of technical specifications and important parts

The proposed QFD-TRIZ integration methodology in **Figure 5** which is used to develop the QFD model combined with a more integrated TRIZ can be described in

2.To identify product variables that will be used as question items for the

3.To identify customers' complaints through a questionnaire survey to obtain

4.To identify customers' desires by distributing open questionnaires based on information obtained from customers' complaint questionnaires and to

In this phase, customers need to identify product variables uses a questionnaire

5.Test the content validity and reliability of the questionnaire through the criterion related validity test, and test reliability with Alpha Cronbachs' coefficient

objectively uses the technique of Brainstorming. The next step is to design the proposed concept of an appropriate integration method in dealing with the times that occur in the QFD process of the first and second phases, especially in addressing the contradiction between the technical specification variable (phase one of QFD) and the critical part variable (phase two QFD). This integration built through a combination of the QFD framework with the TRIZ method in a systematic and more integrated manner. Stages of the proposed Integrated QFD (IQFD)-TRIZ

**30**

**Figure 4.**

*Novel Integration model of QFD-TRIZ.*


Meanwhile, the existing integration methodology of QFD-TRIZ model by Melgoza et al. [52], was used as the development of a Novel integration model developed in the study (see **Figure 5**). The QFD integration methodology steps with the TRIZ model have been selected to be compared with the Novel IQFD-TRIZ. The stages of the existing model as follows:

1.Identification of customer requirements matrix

In this phase, customer voice recognition to know what customers want in terms of product design is done through the product survey. The data are qualitative data.

2.Engineer's voice matrix identification

In this phase, defining the technical specifications of the existing IQFD – TRIZ method is done uses Interviews.

3.Identification of the interest phase of the customer change

This phase uses a questionnaire with the Likert scale.

4.Determine the phase relationship matrix

In this phase, the relationship between customer requirements and technical specifications uses the 5,4,3,2,1 scale.

5.Determine the phase correlation matrix

6.In this phase, the relationship between each technical specification uses the 9,3,1 scale.

## **Figure 5.** *Matrix of QFD-TRIZ model by Melgoza et al. [52].*

The main focus of this research is to optimize the performance of the QFD process integrated with quantitative design techniques, namely comparing novel QFD integration components (novel IQFD) with existing QFD integration (IQFD). The combination built into this research is compared to the existing one developed by previous research. The components of the novel IQFD methodology framework developed in this research maximize product design models efficiently, structurally, quantitatively, systematically, and propose new solutions in designing new products and company products maximizes the functionality of the TRIZ model into the QFD process.

Meanwhile, the weights and scales used in the novel IQFD use the 9,3,1.0 scale much better than the existing IQFD uses the + 2, +1, 0 −1, −2, 4, 3, 2, 1 scale, 0 and scale 5, 4, 3, 2, 1.0. Besides, to identify customer requirements, technical specifications and critical parts of the novel QFD component use the "Brainstorming" method, and the resulting changes are more relevant than the existing QFD use "Interview" method that performed by only one person in the company. The contribution is used to stimulate open discussion of product creative ideas and improvements made to various sources of information, namely, research, specialist, marketing, sales, production, and management.

## *4.4.2 Discussion the difference of novel QFD framework compared to existing QFD*

Some previous studies have discussed QFD integration with various quantitative design techniques models to overcome the constraints of the QFD process in each of its phases. However, there are still limitations in its implementation. This section will discuss in stages how to use the novel IQFD framework component in comparison to the existing IQFD conducted by previous research.

How to use the novel IQFD-TRIZ framework built in this research in comparison to the existing IQFD-TRIZ developed by Melgoza et al. [52], can be seen in **Tables 2** and **3**, as follows.

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*Integrated Model of Product Design Methods DOI: http://dx.doi.org/10.5772/intechopen.92059*

Customer requirements

Technical specifications

**Table 2.**

**Table 2** above can be described as follows:

Triz solution Emphasized after analysis of target matrix and technical specification priority

Extra space/matrix 1.Technical specification contribution 2.Triz solution

interests.

1.Identifying customer requirements of the Novel IQFD-TRIZ and IQFD-TRIZ models uses questionnaires to determine product design changes. After identifying the customers' needs, it defines the importance of the customers' requirements. Novel IQFD-TRIZ built on this research, and the existing IQFD-

**Item Novel IQFD-TRIZ Existing IQFD-TRIZ**

Relationship matrix Weight 9,3,1,0 Weight 5,4,3,2,1 Correlation matrix Weight 9,3,1,0 Weight 9,3,1

Conventional QFD Conventional QFD

The burden of suffering Interview method

Emphasized after correlation

matrix analysis

Triz solution

2.Identification of technical specifications (phase one), and weighting of

After identifying customer requirements, technical specification is implemented with Brainstorming method used in the proposed IQFD-TRIZ, while

As discussed earlier, it is found that customers' voice is still qualitative, that is, measurable and often misleading, systematic. At the same time, the product function requirements are too complex and, therefore, not easy to determine. Changing customer requirements translated into technical specifications obtained from companies is still unclear, too subjective, difficult to verify and only expressed in linguistic form, so the study emphasized that the problem is solved mainly in phase one and phase two, then the novel IQFD framework component built in this research uses Brainstorming method, compared to the

Brainstorming methods focus on companies, from top management to middle management, are involved in defining technical specifications and critical areas, making these changes more objective than those of the incorporation rules. Existing QFDs, where technical specifications and critical parts are made to one person from the company, the production manager, make the changes more subjective. Through the use of Brainstorming, it is possible to obtain more specific technical and critical properties than just one person, the production division manager. The focus of Brainstorming in defining technical specifications and critical parts of the novel QFD framework components is to analyze the translation of customer requirements into technical specifications in phase one and also technical specifications into critical parts in phase two objectively, efficient and accurate translation. Also, the identification of technical specifications and critical parts with Brainstorming can identify product

TRIZ developed by Melgoza et al. [52] uses a Likert scale

*The differences in the novel IQFD-Triz analysis vs the existing IQFD-Triz (Phase One).*

the existing IQFD-TRIZ uses the interview method.

existing IQFD that uses the interview method.

changes that are in line with customer requirements.


## **Table 2.**

*Product Design*

*QFD*

*Matrix of QFD-TRIZ model by Melgoza et al. [52].*

**Figure 5.**

QFD process.

marketing, sales, production, and management.

son to the existing IQFD conducted by previous research.

*4.4.1 Discussion of how to use the novel QFD framework compared to existing* 

The main focus of this research is to optimize the performance of the QFD process integrated with quantitative design techniques, namely comparing novel QFD integration components (novel IQFD) with existing QFD integration (IQFD). The combination built into this research is compared to the existing one developed by previous research. The components of the novel IQFD methodology framework developed in this research maximize product design models efficiently, structurally, quantitatively, systematically, and propose new solutions in designing new products and company products maximizes the functionality of the TRIZ model into the

Meanwhile, the weights and scales used in the novel IQFD use the 9,3,1.0 scale much better than the existing IQFD uses the + 2, +1, 0 −1, −2, 4, 3, 2, 1 scale, 0 and scale 5, 4, 3, 2, 1.0. Besides, to identify customer requirements, technical specifications and critical parts of the novel QFD component use the "Brainstorming" method, and the resulting changes are more relevant than the existing QFD use "Interview" method that performed by only one person in the company. The contribution is used to stimulate open discussion of product creative ideas and improvements made to various sources of information, namely, research, specialist,

*4.4.2 Discussion the difference of novel QFD framework compared to existing QFD*

Some previous studies have discussed QFD integration with various quantitative design techniques models to overcome the constraints of the QFD process in each of its phases. However, there are still limitations in its implementation. This section will discuss in stages how to use the novel IQFD framework component in compari-

How to use the novel IQFD-TRIZ framework built in this research in comparison to the existing IQFD-TRIZ developed by Melgoza et al. [52], can be seen in **Tables 2**

**32**

and **3**, as follows.

*The differences in the novel IQFD-Triz analysis vs the existing IQFD-Triz (Phase One).*

**Table 2** above can be described as follows:


After identifying customer requirements, technical specification is implemented with Brainstorming method used in the proposed IQFD-TRIZ, while the existing IQFD-TRIZ uses the interview method.

As discussed earlier, it is found that customers' voice is still qualitative, that is, measurable and often misleading, systematic. At the same time, the product function requirements are too complex and, therefore, not easy to determine. Changing customer requirements translated into technical specifications obtained from companies is still unclear, too subjective, difficult to verify and only expressed in linguistic form, so the study emphasized that the problem is solved mainly in phase one and phase two, then the novel IQFD framework component built in this research uses Brainstorming method, compared to the existing IQFD that uses the interview method.

Brainstorming methods focus on companies, from top management to middle management, are involved in defining technical specifications and critical areas, making these changes more objective than those of the incorporation rules. Existing QFDs, where technical specifications and critical parts are made to one person from the company, the production manager, make the changes more subjective. Through the use of Brainstorming, it is possible to obtain more specific technical and critical properties than just one person, the production division manager. The focus of Brainstorming in defining technical specifications and critical parts of the novel QFD framework components is to analyze the translation of customer requirements into technical specifications in phase one and also technical specifications into critical parts in phase two objectively, efficient and accurate translation. Also, the identification of technical specifications and critical parts with Brainstorming can identify product changes that are in line with customer requirements.

3.Define the relationship (relationship matrix) between customer requirements and technical specifications (phase one).

Subsequently, defining the relationship between customer requirements and technical specifications, in phase one, and technical specification relationships with critical parts in phase two were performed on the Novel IQFD-TRIZ uses 9,3,1.0. Meanwhile, the existing IQFD-TRIZ model developed by Melgoza et al. [52] uses a scale of 5,4,3,2,1.

4.Define the correlation matrix between technical specifications (phase one).

Correlations between technical specifications in phase one of the novel IQFD-TRIZ model were performed uses the 9,3,1.0 scale, whereas the existing IQFD-TRIZ model uses the 9,3,1 scale.

5.Resolving conflicting differences between technical specifications.

In this phase, the TRIZ method was applied to resolve conflicting issues that occur in phase one QFD. Conflict resolution was performed in various stages, namely (1) defining specific problems, (2) defining common problems uses a 39 × 39 matrix conflict table, (3) identifying joint solving uses 40 TRIZ rules.

6.Define planning matrix (phase one).

After resolving the conflicts in the correlation matrix, then the novel QFD-TRIZ and the existing perform an analysis of customer requirements and the technical specification to prevent design changes in the next phase. In phase two of the QFD, by completing the calculation and determination of the matrix planning, which defines the planned weight loss, calculates the ratio of improvement value to ratio, absolute weight, and relative weight.

7.Define target and technical specification priority (phase one).

The goals and priorities of the critical sections of the novel IQFD-TRIZ and the existing IQFD-TRIZ are both defined to assess the importance of which critical parts are of the highest weight, the difficulty level in designing the smallest product, and the lowest design cost.

Meanwhile, for the differences between the novel IQFD-TRIZ compared to the two-phase IQFD-TRIZ can be seen in **Table 3** as follows:


**35**

*Integrated Model of Product Design Methods DOI: http://dx.doi.org/10.5772/intechopen.92059*

and critical part (phase two).

(phase two).

similar to the scale used in phase one.

4.Define planning matrix (phase two).

*4.4.3 The contribution for knowledge*

critical parts.

matters.

this new knowledge is described as follows:

one QFD and critical phase in phase two QFD.

**Table 3** above can be described as follows:

the novel IQFD-TRIZ uses the Likert scale.

1.Identification of critical parts (phase two), and weighting of interests.

The identification of the critical part in phase two of the novel IQFD-TRIZ was made uses the Brainstorming method. In contrast, in the existing IQFD-TRIZ model developed by Melgoza et al. [52] uses the interview method. Meanwhile,

2.Define the relationship (relationship matrix) between technical specification

Defining the correlation matrix in phase two of the novel IQFD-TRIZ model uses the 9,3,1,0 scale, and the 5,4,3,2,1 scale built on the existing IQFD-TRIZ, is

After obtaining the variables that are in line with the customers' needs, the next step is to perform a correlation analysis between the variables of the qualitative components defined uses the 9,3,1.0 scale in the model developed in this research. Meanwhile, in the model developed by Melgoza et al. [52] used a scale of 5,4,3,2,1.

Subsequent to defines the correlation matrix further performs customer requirements analysis and technical specifications to prevent any product design changes in the next phase, in phase two or phase three and phase four, which is done by calculating and determining the design matrix, which defines weight planned interest, calculating the ratio of the value of the

The target matrix and the critical phase priority in phase two are defined just as they were in phase one of the novel IQFD-TRIZ or the existing IQFD-TRIZ.

Discussions on the contribution of new knowledge have been discussed through the comprehensive and phases of the QFD matrix. The proof of the contribution of

1.The design of an integrated QFD integration development framework that is oriented to customer emotional satisfaction, technical specifications, and

2.QFD phases become more objective by facilitating the calculation of subjective

3.Brainstorming method allow the idea to fill technical specifications in phase

3.Define correlation (correlation matrix) between critical parts

improvement ratio, absolute weight, and relative weight.

5.Define the target and priority of the critical parts (phase two).

## **Table 3.**

*The differences analysis of novel IQFD-Triz vs the existing IQFD-Triz (Phase Two).*

*Product Design*

3.Define the relationship (relationship matrix) between customer requirements

Subsequently, defining the relationship between customer requirements and technical specifications, in phase one, and technical specification relationships with critical parts in phase two were performed on the Novel IQFD-TRIZ uses 9,3,1.0. Meanwhile, the existing IQFD-TRIZ model developed by Melgoza et al.

4.Define the correlation matrix between technical specifications (phase one). Correlations between technical specifications in phase one of the novel IQFD-TRIZ model were performed uses the 9,3,1.0 scale, whereas the

In this phase, the TRIZ method was applied to resolve conflicting issues that occur in phase one QFD. Conflict resolution was performed in various stages, namely (1) defining specific problems, (2) defining common problems uses a 39 × 39 matrix conflict table, (3) identifying joint solving uses 40 TRIZ rules.

After resolving the conflicts in the correlation matrix, then the novel QFD-TRIZ and the existing perform an analysis of customer requirements and the technical specification to prevent design changes in the next phase. In phase two of the QFD, by completing the calculation and determination of the matrix planning, which defines the planned weight loss, calculates the ratio of

The goals and priorities of the critical sections of the novel IQFD-TRIZ and the existing IQFD-TRIZ are both defined to assess the importance of which critical parts are of the highest weight, the difficulty level in designing the smallest

Meanwhile, for the differences between the novel IQFD-TRIZ compared to the

**Item Novel IQFD-TRIZ Existing IQFD-TRIZ** Critical part Brainstorming method Interview method

Extra space/matrix 1. Brainstorming on critical part Triz solution on correlation

Correlation matrix Weight 9,3,1,0 Weight 9,3,1

Weight 9,3,1,0 Weight 5,4,3,2,1

Emphasized after correlation

matrix

matrix

5.Resolving conflicting differences between technical specifications.

improvement value to ratio, absolute weight, and relative weight.

7.Define target and technical specification priority (phase one).

and technical specifications (phase one).

existing IQFD-TRIZ model uses the 9,3,1 scale.

6.Define planning matrix (phase one).

product, and the lowest design cost.

two-phase IQFD-TRIZ can be seen in **Table 3** as follows:

Triz solution Emphasized after the target matrix and critical section priority

*The differences analysis of novel IQFD-Triz vs the existing IQFD-Triz (Phase Two).*

[52] uses a scale of 5,4,3,2,1.

**34**

**Table 3.**

Relationship matrix

**Table 3** above can be described as follows:

1.Identification of critical parts (phase two), and weighting of interests.

The identification of the critical part in phase two of the novel IQFD-TRIZ was made uses the Brainstorming method. In contrast, in the existing IQFD-TRIZ model developed by Melgoza et al. [52] uses the interview method. Meanwhile, the novel IQFD-TRIZ uses the Likert scale.

2.Define the relationship (relationship matrix) between technical specification and critical part (phase two).

Defining the correlation matrix in phase two of the novel IQFD-TRIZ model uses the 9,3,1,0 scale, and the 5,4,3,2,1 scale built on the existing IQFD-TRIZ, is similar to the scale used in phase one.

3.Define correlation (correlation matrix) between critical parts (phase two).

After obtaining the variables that are in line with the customers' needs, the next step is to perform a correlation analysis between the variables of the qualitative components defined uses the 9,3,1.0 scale in the model developed in this research. Meanwhile, in the model developed by Melgoza et al. [52] used a scale of 5,4,3,2,1.

4.Define planning matrix (phase two).

Subsequent to defines the correlation matrix further performs customer requirements analysis and technical specifications to prevent any product design changes in the next phase, in phase two or phase three and phase four, which is done by calculating and determining the design matrix, which defines weight planned interest, calculating the ratio of the value of the improvement ratio, absolute weight, and relative weight.

5.Define the target and priority of the critical parts (phase two).

The target matrix and the critical phase priority in phase two are defined just as they were in phase one of the novel IQFD-TRIZ or the existing IQFD-TRIZ.

## *4.4.3 The contribution for knowledge*

Discussions on the contribution of new knowledge have been discussed through the comprehensive and phases of the QFD matrix. The proof of the contribution of this new knowledge is described as follows:


## **5. Conclusion**

QFD flexibility has facilitated integration with other engineering design tools. This research developed a framework of phase one and phase two of QFD combined Triz, in which the concept developed in this research based on previous studies. The framework components of novel QFD-Triz can effectively overcome the drawbacks while increasing QFD analysis in every phase, and also, there is a new procedure in product design. A novel of QFD framework developed in this study has the potential to be the best technique for designing quality from the customers' point of view. It is believed that this study will provide some research opportunities, for example, emphasizing on improving QFD capabilities and raising problems related to the product design.

## **Author details**

Rosnani Ginting Department of Industrial Engineering, Universitas Sumatera Utara, Medan, Indonesia

\*Address all correspondence to: rosnani@usu.ac.id

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**37**

*Integrated Model of Product Design Methods DOI: http://dx.doi.org/10.5772/intechopen.92059*

[1] Prasad K, Chakraborty S. A quality function deployment-based model for cutting fluid selection. Advances in Tribology. 2016;**2016** 3978102,

[10] Yeh CH, Jay CY, Huang CK, Yu. Integration of four-phase QFD and TRIZ in product R and D: A notebook case study. Research in Engineering

[11] De Felice F. A multiple choice decision analysis: An integrated

[12] Lee-Mortimer A. Managing

to Manufacture. 1995;**2**(5):38-42

QFD–AHP model for the assessment of customer needs. International Journal of Engineering, Science and Technology.

innovation and risk. World Class Design

[13] Li X, Tan KC, Xie M. Optimizing product design using quantitative Quality Function Deployment: a case study. Quality and Reliability Engineering International.

[14] Yamashina H, Ito T, Kawada H. Innovative product development process by integrating QFD with TRIZ. Journal of the Japan Society for Precision Engineering.

Design. 2011;**22**(3):125-141

2010;**2**(9):25-38

2007;**23**(1):45-57

2002;**66**(11):1705-1710

2011;**35**(2011):482-496

2005;**13**(3):232-239

[15] Liu H-T. Product design and selection using fuzzy QFD and fuzzy MCDM approaches. Applied Mathematical Modelling.

[16] Lance NG, Bonollo E. The development of a suite of design methods appropriate for teaching product design. Global Journal of Engineering Education. 2002;**6**(1):45-52

[17] Gonçalves-Coelho AM, Mourão AJF, Pereira ZL. Improving the use of QFD with axiomatic design. Concurrent Engineering: Research and Applications.

[18] Mendoza N, Horacio A, Arturo M. Case studies in the integration of QFD,

[2] Vavdhara AMK, Yadav BJS, Yadav CL, Ghosh DMK. Quality improvement in steel rolling industry through quality function deployment: A case study in Ajmera Steel Rolling, Ratlam. In: International Conference on Current Trends In Technology, 'NUiCONE. 2011.

[3] Olhager J, Bengtsson J. The impact of the product mix on the value of flexibility. Omega. 2020;**30**(4):265-273

[4] Sakao T. A QFD-centred design methodology for environmentally conscious product design. International

Journal of Production Research. 2013;**45**(18-19):4143-4162

[5] Farsijani H, Torabandeh MA. Improvement of efficiency in product designing by usage of fuzzy QFD & TRIZ: A case study on Transfo company. AENSI Journals. Journal of Applied Science and Agriculture.

[6] Yihong L, Yunfei S, Ting C. Study of new wall materials design based on TRIZ integrated innovation method. Management Science and Engineering.

[7] Ulrich KT, Eppinger SD. Product Design and Development. 4th ed. New York: McGraw Hill; 2008

[8] Prasad B. Review of QFD and related deployment techniques. Journal Manufacturing System.

[9] Besterfield DH. Total Quality Management. 2nd ed. New Jersey, Prentice-Hall: Upper Saddle River; 1999

2013;**8**(4):451-461

2012;**6**(4):15-29

1998;**17**(3):221-234

**References**

10 pages

pp. 1-4

*Integrated Model of Product Design Methods DOI: http://dx.doi.org/10.5772/intechopen.92059*

## **References**

*Product Design*

and phase two (seamless transfer)).

bined technique.

**5. Conclusion**

product design.

**Author details**

Rosnani Ginting

Indonesia

existing models for each type of QFD combination.

4.The methodology combined with QFD is the same for both phases and facilitates learning and application (the same data can be used for QFD phase one

5.House of QFD model more integrated between phase one and phase two than

6.House of QFD on phase one and phase two are different for each type of QFD combination where the focus aspects of product design will follow the com-

QFD flexibility has facilitated integration with other engineering design tools. This research developed a framework of phase one and phase two of QFD combined Triz, in which the concept developed in this research based on previous studies. The framework components of novel QFD-Triz can effectively overcome the drawbacks while increasing QFD analysis in every phase, and also, there is a new procedure in product design. A novel of QFD framework developed in this study has the potential to be the best technique for designing quality from the customers' point of view. It is believed that this study will provide some research opportunities, for example, emphasizing on improving QFD capabilities and raising problems related to the

Department of Industrial Engineering, Universitas Sumatera Utara, Medan,

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to: rosnani@usu.ac.id

provided the original work is properly cited.

**36**

[1] Prasad K, Chakraborty S. A quality function deployment-based model for cutting fluid selection. Advances in Tribology. 2016;**2016** 3978102, 10 pages

[2] Vavdhara AMK, Yadav BJS, Yadav CL, Ghosh DMK. Quality improvement in steel rolling industry through quality function deployment: A case study in Ajmera Steel Rolling, Ratlam. In: International Conference on Current Trends In Technology, 'NUiCONE. 2011. pp. 1-4

[3] Olhager J, Bengtsson J. The impact of the product mix on the value of flexibility. Omega. 2020;**30**(4):265-273

[4] Sakao T. A QFD-centred design methodology for environmentally conscious product design. International Journal of Production Research. 2013;**45**(18-19):4143-4162

[5] Farsijani H, Torabandeh MA. Improvement of efficiency in product designing by usage of fuzzy QFD & TRIZ: A case study on Transfo company. AENSI Journals. Journal of Applied Science and Agriculture. 2013;**8**(4):451-461

[6] Yihong L, Yunfei S, Ting C. Study of new wall materials design based on TRIZ integrated innovation method. Management Science and Engineering. 2012;**6**(4):15-29

[7] Ulrich KT, Eppinger SD. Product Design and Development. 4th ed. New York: McGraw Hill; 2008

[8] Prasad B. Review of QFD and related deployment techniques. Journal Manufacturing System. 1998;**17**(3):221-234

[9] Besterfield DH. Total Quality Management. 2nd ed. New Jersey, Prentice-Hall: Upper Saddle River; 1999 [10] Yeh CH, Jay CY, Huang CK, Yu. Integration of four-phase QFD and TRIZ in product R and D: A notebook case study. Research in Engineering Design. 2011;**22**(3):125-141

[11] De Felice F. A multiple choice decision analysis: An integrated QFD–AHP model for the assessment of customer needs. International Journal of Engineering, Science and Technology. 2010;**2**(9):25-38

[12] Lee-Mortimer A. Managing innovation and risk. World Class Design to Manufacture. 1995;**2**(5):38-42

[13] Li X, Tan KC, Xie M. Optimizing product design using quantitative Quality Function Deployment: a case study. Quality and Reliability Engineering International. 2007;**23**(1):45-57

[14] Yamashina H, Ito T, Kawada H. Innovative product development process by integrating QFD with TRIZ. Journal of the Japan Society for Precision Engineering. 2002;**66**(11):1705-1710

[15] Liu H-T. Product design and selection using fuzzy QFD and fuzzy MCDM approaches. Applied Mathematical Modelling. 2011;**35**(2011):482-496

[16] Lance NG, Bonollo E. The development of a suite of design methods appropriate for teaching product design. Global Journal of Engineering Education. 2002;**6**(1):45-52

[17] Gonçalves-Coelho AM, Mourão AJF, Pereira ZL. Improving the use of QFD with axiomatic design. Concurrent Engineering: Research and Applications. 2005;**13**(3):232-239

[18] Mendoza N, Horacio A, Arturo M. Case studies in the integration of QFD, VE and DFMA during the product design stage. In: The Proceedings of the 9th International Conference of Concurrent Enterprising, Espoo, Finland. 2003. pp. 16-18

[19] Kant S, Hundal GS. Product design through QFD approach with hybrid of AHP and Fuzzy Logics. 21 International Proceedings of Economics Development and Research; 2014. pp. 112-116

[20] Naseri K. Algorithm for costoptimized QFD decision-making problem. Evolutionary Intelligence. 2014;**9**(1-2):21-36

[21] Yang CL, Huang RH, Wei WL. A modified TRIZ for new product development management. Business and Information (Sapporo, July 3-5). 2012:323-334

[22] Gupta S, Okudan GW. Computeraided generation of modularised conceptual designs with assembly and variety considerations. Journal of Engineering Design. 2012:**19**(6):533-551

[23] Pourhasomia MH, Khamseh AA, Ghorbanzad Y. A hybrid of Kano and QFD for ranking customers' preferences: A case study of bank melli iran. Management Science Letters. 2013;**3**:845-860

[24] Lai CJ, Hsu CH, Kuo HM. An empirical study of constructing a dynamic mining and forecasting system for the life cycle assessment-based green supply chain. Wseas Transactions On Systems. 2012;**11**(4):129-139. ISSN: 2224-2678

[25] Revelle, Jack B. The Hand Book of QFD. United States: Acid Free Paper. 1997

[26] Bouchereau V, Rowlands H. Methods and techniques to help quality function deployment (QFD). Benchmarking: An International Journal. 2000;**7**(1):8-19

[27] Kao HP, Su E, Wang B. IQFD: A blackboard-based multi-agent system for supporting concurrent engineering projects. International Journal of Production Research. 2002;**40**(5):1235-1262

[28] Hsing CH, Lo CH, Tseng KC. One-Step QFD based 3D morphological charts for concept generation of product variant design. Expert Systems with Applications. 2010;**37**:7351-7363

[29] Cohen L. Quality Function Deployment: How to make QFD work for you. Addison-Wesley. 1995

[30] Hales R, Staley D. Mix target costing, QFD for successful new products. Marketing News. 1995;**29**(1):18

[31] Hauser JR, Clausing DP. The House of Quality. Harvard Business Review, May/June. 1998

[32] Griffin A, Hauser JR. The voice of the customer. Marketing Science. 1998;**12**(1)

[33] Mehta P. Designed chip embeds user concerns. In: Electronic Engineering Times. 1994 January 24

[34] Chan LK, Wu ML. Quality function deployment: A literature review. European Journal Operation Research. 2002;**143**:463-497

[35] Poel IVD. Methodological problems in QFD and directions for future development. Research in Engineering Design. 2007;**18**(1):21-36

[36] Law HW, Hua M. Using quality function deployment in singulation process analysis. Engineering Letters. 2007;**14**:1. EL\_14\_1\_6 (Advance online publication: 12 February 2007), pp. 1-5

[37] Mohammadi F, Sadi MK, Nateghi F, Abdullah A. A hybrid quality function deployment and cybernetic analytic

**39**

2006:1144-1147

*Integrated Model of Product Design Methods DOI: http://dx.doi.org/10.5772/intechopen.92059*

> [46] Regazzoni D, Russo D. TRIZ tools to enhance risk management. TRIZ Future Conference 2010, Procedia Engineering.

[47] Liu S, Shi D, Ying Z. A planning approach of engineering characteristics

[48] Marsot J, Claudon L. Design and Ergonomics. Methods for integrating ergonomics at hand tool design stage. International Journal of Occupational Safety and Ergonomics: JOSE (Impact

[49] Rau H, Fang YT. Conflict resolution of product package design for logistics using the TRIZ method. Machine Learning and Cybernetics. International

Conference. 2009;(5):2891-2896

[50] David, Novoa DR, Rovira LN, Aguayo TH, Said. Inventive problem solving based on dialectical negation, using evolutionary algorithms and TRIZ heuristics. Computers in Industry.

[51] Butdee ST. QFDE combined with TRIZ framework to formulate and respond to functional design for a high temperature machine (htm). C. Asian International Journal of Science and Technology in Production and Manufacturing Engineering.

[52] Melgoza EL, Serenó L, Rosell A, Ciurana J. An integrated parameterized tool for designing a customized tracheal

[53] Shih HS, Chen SH. A conceptual design of a mobile healthcare device-an application of three-stage QFD with ANP and TRIZ. International Journal of Operations Research. 2013;**10**(2):

stent. Computer-Aided Design.

2012;**44**(12):1173-1181

80-91

2010;**4**(62):437-445

2010;**3**(4):77-84

Factor). 2004;**10**(1):13-23

based on QFD-TRIZ integrated. RIFIP AICT 304. IFIP International Federation for Information Processing.

2010;**9**:40-51

2009:117-126

network process model for project manager selection. Journal of Civil Engineering and Management. 2014,

[38] Akao Y, Mazur GH. The leading edge in QFD: Past, present and future. International Journal of Quality & Reliability Management.

[39] Brad S. Complex system design technique (CSDT). International Journal of Production Research.

[40] Kim KJ, Moskowitz H, Dhingra A, Evans G. Fuzzy multi-criteria models for quality function deployment. European Journal Operation Research.

optimization models for implementing

[42] Kazemzadeh RB, Behzadian M, Aghdasi M, Albadvi A. Integration of marketing research techniques into house of quality and product family design. The International Journal of Advanced Manufacturing Technology.

[43] Sarno E, Kumar V, Li W. A hybrid methodology for enhancing reliability of large systems in conceptual design and its application to the design of a multiphase flow station. Research in Engineering Design. 2005;**1-2**(16):27-41

[44] Wang H, Chen G, Lin Z. Algorithm of Integrating QFD and TRIZ for the innovative design process. International

Journal Computer Application Technology. 2005;**23**(1):41-52

[45] Lu C, Liao Z, Jiang SH, Lin G. Research on Innovative Product design system based on QFD and TRIZ. China Trans Technology Publication.

2014;**20**(6):795-809

2003;**20**(1):20-35

2000;**121**(3):504-518

[41] Zhou M. Fuzzy logic and

QFD. Computers & Industrial Engineering. 1998;**35**(1-2):237-240

2009;**41**(9-10):1019-1033

2004:1-28

*Integrated Model of Product Design Methods DOI: http://dx.doi.org/10.5772/intechopen.92059*

*Product Design*

VE and DFMA during the product design stage. In: The Proceedings of the 9th International Conference of Concurrent Enterprising, Espoo, [27] Kao HP, Su E, Wang B. IQFD: A blackboard-based multi-agent system for supporting concurrent engineering projects. International Journal of Production Research.

[28] Hsing CH, Lo CH, Tseng KC. One-Step QFD based 3D morphological charts for concept generation of product variant design. Expert Systems with Applications. 2010;**37**:7351-7363

[29] Cohen L. Quality Function Deployment: How to make QFD work

for you. Addison-Wesley. 1995

1995;**29**(1):18

May/June. 1998

Times. 1994 January 24

2002;**143**:463-497

Design. 2007;**18**(1):21-36

1998;**12**(1)

[30] Hales R, Staley D. Mix target costing, QFD for successful new products. Marketing News.

[31] Hauser JR, Clausing DP. The House of Quality. Harvard Business Review,

[32] Griffin A, Hauser JR. The voice of the customer. Marketing Science.

[33] Mehta P. Designed chip embeds user concerns. In: Electronic Engineering

[34] Chan LK, Wu ML. Quality function deployment: A literature review. European Journal Operation Research.

[35] Poel IVD. Methodological problems in QFD and directions for future development. Research in Engineering

[36] Law HW, Hua M. Using quality function deployment in singulation process analysis. Engineering Letters. 2007;**14**:1. EL\_14\_1\_6 (Advance online publication: 12 February 2007), pp. 1-5

[37] Mohammadi F, Sadi MK, Nateghi F, Abdullah A. A hybrid quality function deployment and cybernetic analytic

2002;**40**(5):1235-1262

[19] Kant S, Hundal GS. Product design through QFD approach with hybrid of AHP and Fuzzy Logics. 21 International Proceedings of Economics Development

and Research; 2014. pp. 112-116

[20] Naseri K. Algorithm for costoptimized QFD decision-making problem. Evolutionary Intelligence.

[21] Yang CL, Huang RH, Wei WL. A modified TRIZ for new product development management. Business and Information (Sapporo, July 3-5).

[22] Gupta S, Okudan GW. Computeraided generation of modularised conceptual designs with assembly and variety considerations. Journal of Engineering Design. 2012:**19**(6):533-551

[23] Pourhasomia MH, Khamseh AA, Ghorbanzad Y. A hybrid of Kano and QFD for ranking customers' preferences: A case study of bank melli iran. Management Science Letters.

[24] Lai CJ, Hsu CH, Kuo HM. An empirical study of constructing a dynamic mining and forecasting system for the life cycle assessment-based green supply chain. Wseas Transactions On Systems. 2012;**11**(4):129-139. ISSN:

[25] Revelle, Jack B. The Hand Book of QFD. United States: Acid Free Paper.

[26] Bouchereau V, Rowlands H. Methods and techniques to help quality function deployment (QFD). Benchmarking: An International

Journal. 2000;**7**(1):8-19

Finland. 2003. pp. 16-18

2014;**9**(1-2):21-36

2012:323-334

2013;**3**:845-860

2224-2678

1997

**38**

network process model for project manager selection. Journal of Civil Engineering and Management. 2014, 2014;**20**(6):795-809

[38] Akao Y, Mazur GH. The leading edge in QFD: Past, present and future. International Journal of Quality & Reliability Management. 2003;**20**(1):20-35

[39] Brad S. Complex system design technique (CSDT). International Journal of Production Research. 2004:1-28

[40] Kim KJ, Moskowitz H, Dhingra A, Evans G. Fuzzy multi-criteria models for quality function deployment. European Journal Operation Research. 2000;**121**(3):504-518

[41] Zhou M. Fuzzy logic and optimization models for implementing QFD. Computers & Industrial Engineering. 1998;**35**(1-2):237-240

[42] Kazemzadeh RB, Behzadian M, Aghdasi M, Albadvi A. Integration of marketing research techniques into house of quality and product family design. The International Journal of Advanced Manufacturing Technology. 2009;**41**(9-10):1019-1033

[43] Sarno E, Kumar V, Li W. A hybrid methodology for enhancing reliability of large systems in conceptual design and its application to the design of a multiphase flow station. Research in Engineering Design. 2005;**1-2**(16):27-41

[44] Wang H, Chen G, Lin Z. Algorithm of Integrating QFD and TRIZ for the innovative design process. International Journal Computer Application Technology. 2005;**23**(1):41-52

[45] Lu C, Liao Z, Jiang SH, Lin G. Research on Innovative Product design system based on QFD and TRIZ. China Trans Technology Publication. 2006:1144-1147

[46] Regazzoni D, Russo D. TRIZ tools to enhance risk management. TRIZ Future Conference 2010, Procedia Engineering. 2010;**9**:40-51

[47] Liu S, Shi D, Ying Z. A planning approach of engineering characteristics based on QFD-TRIZ integrated. RIFIP AICT 304. IFIP International Federation for Information Processing. 2009:117-126

[48] Marsot J, Claudon L. Design and Ergonomics. Methods for integrating ergonomics at hand tool design stage. International Journal of Occupational Safety and Ergonomics: JOSE (Impact Factor). 2004;**10**(1):13-23

[49] Rau H, Fang YT. Conflict resolution of product package design for logistics using the TRIZ method. Machine Learning and Cybernetics. International Conference. 2009;(5):2891-2896

[50] David, Novoa DR, Rovira LN, Aguayo TH, Said. Inventive problem solving based on dialectical negation, using evolutionary algorithms and TRIZ heuristics. Computers in Industry. 2010;**4**(62):437-445

[51] Butdee ST. QFDE combined with TRIZ framework to formulate and respond to functional design for a high temperature machine (htm). C. Asian International Journal of Science and Technology in Production and Manufacturing Engineering. 2010;**3**(4):77-84

[52] Melgoza EL, Serenó L, Rosell A, Ciurana J. An integrated parameterized tool for designing a customized tracheal stent. Computer-Aided Design. 2012;**44**(12):1173-1181

[53] Shih HS, Chen SH. A conceptual design of a mobile healthcare device-an application of three-stage QFD with ANP and TRIZ. International Journal of Operations Research. 2013;**10**(2): 80-91

[54] Patel A, Deshpande V. A review: quality improvement through the integration of six sigma, QFD and TRIZ in manufacturing industries. Industrial Engineering Journal. 2016;**9**(10):34-39

**Chapter 3**

**Abstract**

**1. Reverse engineering**

**2. Photogrammetry**

of a product in almost every industry [3].

photogrammetry stands out for its ease of use and low cost.

operations.

**41**

Photogrammetry as an

Engineering Design Tool

its great possibilities of application in the engineering field.

*Ana Pilar Valerga Puerta, Rocio Aletheia Jimenez-Rodriguez,*

Photogrammetry is a technique used for studying and precisely defining the shape, dimension, and position in space of any object, using mainly measurements taken over one or more photographs of that object. Today, photogrammetry is a popular science due to its ease of application, low cost, and good results. Based on these causes, it is becoming a good alternative to scanning. This has led to its implementation in different sectors such as the archeological, architectural, and topographical for application in element reconstructions, cartography, or biomechanics. This chapter presents the fundamental aspects of this technology, as well as

**Keywords:** 3D scan, reverse engineering, 3D design, point cloud, CAD, virtual model, 3D reconstruction, virtual assembly, augmented reality, virtual reality

Reverse engineering is based on the study of certain principles and information of a product. The main function of reverse engineering is to obtain the maximum information about an element or device, including its geometry and appearance, among other things [1, 2]. Its first appearance was around World War II, in military

The field of application of this type of engineering is very wide, highlighting the 3D digitalization used mainly for research, analysis, and reasoning of the technology used by other companies, for the development of elements without making use of specific information (redesign), and for the tasks of inspection or virtual metrology

The main 3D digitization technologies are shown in **Figure 1**, among which

Photogrammetry is distinguished by the measurement on photographs, allowing to obtain from any object its real dimensions, position, shape, and textures [4, 5]. These processes or this science emerged in the middle of the nineteenth century, being as old as photography. The first photogrammetric device and the first methodology were created in 1849 by the Frenchman Aimé Laussedat. He, "the father of

*Sergio Fernandez-Vidal and Severo Raul Fernandez-Vidal*

[55] Santos MD, Fernandes MdC, dos Santos FMC, Dias FdC., Almeida JJ, Júnior JA. An approach of triz methodology with inventive solutions for toys used by children with special needs based on the requirements of quality house (QFD). IOSR Journal of Engineering (IOSRJEN). ISSN. 2016;(12): 2278-8719. ISSN (e): 2250-3021

[56] Suzianti A, Yudha IS, Anjani S, Handana IG. Designing laptop based on costumer preference using ECQFD and TRIZ methods. 6th International Conference on Industrial Engineering and Operations Management in Kuala Lumpur. 2016;**8**(10):1132-1138

## **Chapter 3**

*Product Design*

2250-3021

[54] Patel A, Deshpande V. A review: quality improvement through the integration of six sigma, QFD and TRIZ in manufacturing industries. Industrial Engineering Journal. 2016;**9**(10):34-39

[55] Santos MD, Fernandes MdC, dos Santos FMC, Dias FdC., Almeida JJ, Júnior JA. An approach of triz methodology with inventive solutions for toys used by children with special needs based on the requirements of quality house (QFD). IOSR Journal of Engineering (IOSRJEN). ISSN. 2016;(12): 2278-8719. ISSN (e):

[56] Suzianti A, Yudha IS, Anjani S, Handana IG. Designing laptop based on costumer preference using ECQFD and TRIZ methods. 6th International Conference on Industrial Engineering and Operations Management in Kuala

Lumpur. 2016;**8**(10):1132-1138

**40**

## Photogrammetry as an Engineering Design Tool

*Ana Pilar Valerga Puerta, Rocio Aletheia Jimenez-Rodriguez, Sergio Fernandez-Vidal and Severo Raul Fernandez-Vidal*

## **Abstract**

Photogrammetry is a technique used for studying and precisely defining the shape, dimension, and position in space of any object, using mainly measurements taken over one or more photographs of that object. Today, photogrammetry is a popular science due to its ease of application, low cost, and good results. Based on these causes, it is becoming a good alternative to scanning. This has led to its implementation in different sectors such as the archeological, architectural, and topographical for application in element reconstructions, cartography, or biomechanics. This chapter presents the fundamental aspects of this technology, as well as its great possibilities of application in the engineering field.

**Keywords:** 3D scan, reverse engineering, 3D design, point cloud, CAD, virtual model, 3D reconstruction, virtual assembly, augmented reality, virtual reality

## **1. Reverse engineering**

Reverse engineering is based on the study of certain principles and information of a product. The main function of reverse engineering is to obtain the maximum information about an element or device, including its geometry and appearance, among other things [1, 2]. Its first appearance was around World War II, in military operations.

The field of application of this type of engineering is very wide, highlighting the 3D digitalization used mainly for research, analysis, and reasoning of the technology used by other companies, for the development of elements without making use of specific information (redesign), and for the tasks of inspection or virtual metrology of a product in almost every industry [3].

The main 3D digitization technologies are shown in **Figure 1**, among which photogrammetry stands out for its ease of use and low cost.

## **2. Photogrammetry**

Photogrammetry is distinguished by the measurement on photographs, allowing to obtain from any object its real dimensions, position, shape, and textures [4, 5]. These processes or this science emerged in the middle of the nineteenth century, being as old as photography. The first photogrammetric device and the first methodology were created in 1849 by the Frenchman Aimé Laussedat. He, "the father of

Photogrammetric technology is generally based on the illumination of one object and the inclusion of solutions derived from the measurement of conjugated points, appearing in two photographic images or measuring the conjunction of points in multiple photographic images (three or more images). There are different photogrammetric techniques. One of them is to ensure that the surface of the object has enough light and optical texture to allow conjugated dots to be paired through two or more images. In some cases, optical texture can be achieved by projecting a pattern over the surface of the object at the time of image capture [11–13].

The basic mathematical equations underlying photogrammetry, called collinearity equations, are responsible for unifying the coordinate system of the image in the camera with the object being photographed [14] (Eqs. (1)–(3)):

1

perspective center in the object's space; and *pn = (xn, yn)<sup>T</sup>* and *Pn = (Xn, Yn, Zn)T* = target *n* coordinates at the image plane and the space of the object, respectively. The above equation manipulated algebraically produces the well-known collinearity equations that relate the location of destination *nth* in the space of

CA <sup>¼</sup> *<sup>λ</sup><sup>M</sup>*

*Xn* � *X*<sup>0</sup> *Yn* � *Y*<sup>0</sup> *Zn* � *Z*<sup>0</sup>

*m*11ð Þþ *Xn* � *X*<sup>0</sup> *m*12ð Þþ *Yn* � *Y*<sup>0</sup> *m*13ð Þ *Zn* � *Z*<sup>0</sup> *m*31ð Þþ *Xn* � *X*<sup>0</sup> *m*32ð Þþ *Yn* � *Y*<sup>0</sup> *m*33ð Þ *Zn* � *Z*<sup>0</sup>

*m*21ð Þþ *Xn* � *X*<sup>0</sup> *m*22ð Þþ *Yn* � *Y*<sup>0</sup> *m*23ð Þ *Zn* � *Z*<sup>0</sup> *m*31ð Þþ *Xn* � *X*<sup>0</sup> *m*32ð Þþ *Yn* � *Y*<sup>0</sup> *m*33ð Þ *Zn* � *Z*<sup>0</sup>

*m*<sup>11</sup> ¼ cos *ф* cos *к* (4)

*m*<sup>21</sup> ¼ � cos *ф* sin *к* (7)

*m*<sup>31</sup> ¼ sin *ф* (10) *m*<sup>32</sup> ¼ � sin *ω* cos *ф* (11) *m*<sup>33</sup> ¼ cos *ω* cos *ф* (12)

*m*<sup>12</sup> ¼ sin *ω* sin *ф* cos *к* þ cos *ω* sin *к* (5) *m*<sup>13</sup> ¼ � cos *ω* sin *ф* cos *к* þ sin *ω* sin *к* (6)

*m*<sup>22</sup> ¼ � sin *ω* sin *ф* sin *к* þ cos *ω* cos *к* (8) *m*<sup>23</sup> ¼ cos *ω* sin *ф* sin *к* þ sin *ω* cos *к* (9)

where *mij* (*i, j* = 1, 2, 3) = elements of the rotation matrix M which are functions of the Euler orientation angles (*ω, ф, к*), which are essentially the angles of tilt,

The plane of the image can be transformed analytically into its *X*, *Y*, and *Z* coordinates in global space. Photogrammetry is effective and computationally simple. It should be noted that its algorithm is based on definitions of both interior and exterior orientations. In a photographic system, if the internal parameters of a

1

CA (1)

(2)

(3)

0

B@

where *λ* = scaling factor; *M* = rotation matrix; *Xo, Yo*, and *Zo* = the position of the

*xn* � *x*<sup>0</sup> *yn* � *y*<sup>0</sup> �*c*

objects with the corresponding point in the plane of the image:

rotation and rotation of the camera in the object space (Eq. (4)–(11))

0

B@

**3. Fundamentals of photogrammetry**

*Photogrammetry as an Engineering Design Tool DOI: http://dx.doi.org/10.5772/intechopen.92998*

*xn* � *x*<sup>0</sup> ¼ �*c*

*yn* � *y*<sup>0</sup> ¼ �*c*

**43**

photogrammetry," used terrestrial photographs and compiled a topographic map. This method was known as iconometry, which means the art of finding the size of an object by measuring its image. Digital photogrammetry was born in the 1980s, having as a great innovation the use of digital images as a primary data source [6, 7].

The main phases of digital photogrammetry are analysis of the shape of the object and planning of the photos needed to be taken; calibration of the camera; image processing with specific software to generate a cloud of points; and transfer of this point cloud to the CAD software to create a 3D model. The accuracy of the reconstruction depends on the quality of the images and textures. Photogrammetry algorithms typically indicate the problem, such as minimizing the sum of the squares of a set of errors, known as "package fit" [8]. Structure algorithms, from motion (SfM), can find a set of 3D points (P), a rotation (R), and the camera position (t), given a set of images of a static scene with 2D points in correspondence, as shown in **Figure 2** [10].

**Figure 2.** *Structure of the motion algorithm [9].*

*Photogrammetry as an Engineering Design Tool DOI: http://dx.doi.org/10.5772/intechopen.92998*

Photogrammetric technology is generally based on the illumination of one object and the inclusion of solutions derived from the measurement of conjugated points, appearing in two photographic images or measuring the conjunction of points in multiple photographic images (three or more images). There are different photogrammetric techniques. One of them is to ensure that the surface of the object has enough light and optical texture to allow conjugated dots to be paired through two or more images. In some cases, optical texture can be achieved by projecting a pattern over the surface of the object at the time of image capture [11–13].

## **3. Fundamentals of photogrammetry**

The basic mathematical equations underlying photogrammetry, called collinearity equations, are responsible for unifying the coordinate system of the image in the camera with the object being photographed [14] (Eqs. (1)–(3)):

$$
\begin{pmatrix} \boldsymbol{x}\_{\boldsymbol{n}} - \boldsymbol{x}\_{0} \\ \boldsymbol{y}\_{\boldsymbol{n}} - \boldsymbol{y}\_{0} \\ -\boldsymbol{c} \end{pmatrix} = \lambda \mathbf{M} \begin{pmatrix} \boldsymbol{X}\_{\boldsymbol{n}} - \boldsymbol{X}\_{0} \\ \boldsymbol{Y}\_{\boldsymbol{n}} - \boldsymbol{Y}\_{0} \\ \boldsymbol{Z}\_{\boldsymbol{n}} - \boldsymbol{Z}\_{0} \end{pmatrix} \tag{1}
$$

where *λ* = scaling factor; *M* = rotation matrix; *Xo, Yo*, and *Zo* = the position of the perspective center in the object's space; and *pn = (xn, yn)<sup>T</sup>* and *Pn = (Xn, Yn, Zn)T* = target *n* coordinates at the image plane and the space of the object, respectively. The above equation manipulated algebraically produces the well-known collinearity equations that relate the location of destination *nth* in the space of objects with the corresponding point in the plane of the image:

$$\mathbf{x}\_{n} - \mathbf{x}\_{0} = -\boldsymbol{\varepsilon} \frac{m\_{11}(\mathbf{X}\_{n} - \mathbf{X}\_{0}) + m\_{12}(\mathbf{Y}\_{n} - \mathbf{Y}\_{0}) + m\_{13}(\mathbf{Z}\_{n} - \mathbf{Z}\_{0})}{m\_{31}(\mathbf{X}\_{n} - \mathbf{X}\_{0}) + m\_{32}(\mathbf{Y}\_{n} - \mathbf{Y}\_{0}) + m\_{33}(\mathbf{Z}\_{n} - \mathbf{Z}\_{0})} \tag{2}$$

$$y\_n - y\_0 = -c \frac{m\_{21}(X\_n - X\_0) + m\_{22}(Y\_n - Y\_0) + m\_{23}(Z\_n - Z\_0)}{m\_{31}(X\_n - X\_0) + m\_{32}(Y\_n - Y\_0) + m\_{33}(Z\_n - Z\_0)} \tag{3}$$

where *mij* (*i, j* = 1, 2, 3) = elements of the rotation matrix M which are functions of the Euler orientation angles (*ω, ф, к*), which are essentially the angles of tilt, rotation and rotation of the camera in the object space (Eq. (4)–(11))

$$m\_{11} = \cos\phi \cos\kappa \tag{4}$$

$$m\_{12} = \sin\,\,\omega\,\,\sin\,\,\phi\,\,\cos\,\,\kappa + \cos\,\,\omega\,\,\sin\,\,\kappa\tag{5}$$

$$m\_{1\sharp} = -\cos\left(\omega\sin\phi\cos\kappa + \sin\phi\sin\kappa\right) \tag{6}$$

$$m\_{21} = -\cos\phi \sin\kappa \tag{7}$$

$$m\_{22} = -\sin\,\,\omega\,\,\sin\,\,\phi\,\,\sin\,\,\kappa + \cos\,\,\omega\,\,\cos\,\,\kappa\tag{8}$$

$$m\_{2\beta} = \cos\left(\omega\sin\phi\sin\kappa + \sin\phi\cos\kappa\right) \tag{9}$$

$$m\_{31} = \sin \phi \tag{10}$$

$$m\_{32} = -\sin\,\,\alpha\,\,\cos\,\,\phi\tag{11}$$

$$m\_{33} = \cos\,\,\omega\,\,\cos\,\phi\tag{12}$$

The plane of the image can be transformed analytically into its *X*, *Y*, and *Z* coordinates in global space. Photogrammetry is effective and computationally simple. It should be noted that its algorithm is based on definitions of both interior and exterior orientations. In a photographic system, if the internal parameters of a

photogrammetry," used terrestrial photographs and compiled a topographic map. This method was known as iconometry, which means the art of finding the size of an object by measuring its image. Digital photogrammetry was born in the 1980s, having as a great innovation the use of digital images as a primary data source [6, 7]. The main phases of digital photogrammetry are analysis of the shape of the object and planning of the photos needed to be taken; calibration of the camera; image processing with specific software to generate a cloud of points; and transfer of this point cloud to the CAD software to create a 3D model. The accuracy of the reconstruction depends on the quality of the images and textures. Photogrammetry algorithms typically indicate the problem, such as minimizing the sum of the squares of a set of errors, known as "package fit" [8]. Structure algorithms, from motion (SfM), can find a set of 3D points (P), a rotation (R), and the camera position (t), given a set of images of a static scene with 2D points in correspon-

dence, as shown in **Figure 2** [10].

*Classification of 3D scanning technologies.*

**Figure 1.**

*Product Design*

**Figure 2.**

**42**

*Structure of the motion algorithm [9].*

camera are known, any spatial point can be fixed by the intersection of two beams of light that are projected.

There are two main factors that induce photogrammetry measurement errors: System error due to lens distortion and random error due to human factors.

1.System error due to lens distortion. It causes a point in the image in the plane to move from its true position (*x, y*) to a disturbed position. The coordinates of any point in the image can be compensated with Eqs. (13)–(14):

$$
\boldsymbol{\mathfrak{x}}'\_{\boldsymbol{n}} = \boldsymbol{\mathfrak{x}}\_{\boldsymbol{n}} + d\boldsymbol{\mathfrak{x}} \tag{13}
$$

$$\mathbf{y'}\_{\mathfrak{n}} = \mathbf{y}\_{\mathfrak{n}} + d\mathbf{y} \tag{14}$$

In the lens, the largest error occurs at the point of the projected image. Therefore, *dx*, *dy* can be broken down by Eqs. (15)–(16):

$$d\mathfrak{x} = d\mathfrak{x}\_r + d\mathfrak{x}\_d \tag{15}$$

$$dy = dy\_r + dy\_d \tag{16}$$

**5.1 Camera objective**

*Scheme of operation of a camera objective.*

*Photogrammetry as an Engineering Design Tool DOI: http://dx.doi.org/10.5772/intechopen.92998*

**Figure 3.**

and thin as possible [17, 19].

length is shown in **Figure 4**.

sensor as the number becomes smaller [4, 7]:

**5.3 Relative aperture**

length (*f*) (Eq. (17)).

**45**

**5.2 Focal length**

in the wake of aberrations, which add up to force [20].

Included in the optical part of the camera, it is in charge in projecting the image

Its resolving capacity depends on two parameters: aberrations and diffraction. One of the main functions of the objective is to suppress aberrations. When the diaphragm is closed, the aberrations are placated, and the only limiting factor is diffraction. When the diaphragm is opened, diffraction diminishes its significance

This parameter is measured from the optical center of the lens to the focal plane, when the camera focused toward the infinity [5, 21]. Normal lenses are those which have a distance close to the diagonal of the cliché. The representation of the focal

Relative aperture (*Ab*) is the connection of the lens diameter (*D*) and its focal

It is shown by the denominator, known as brightness or "f-number." In a different way, the aperture is the span through which light enters to be captured by the sensor. The more spacious the opening will be, the more light will enter the

that crosses it on the same plane and in outstanding conditions of sharpness. Therefore, it is a matter of focusing on the objects that are at equal distance on the focal plane. From certain distance, all the objects will be projected on the same plane. The light points are transmitted to an element that composes the scenario. As a result of diffraction, this is shown as a circular point with a halo around it and concentric rings, named Airy discs. Suppressing them is unfeasible because it is a physical light effect. Even so, it would be desirable for such rings to be as diffuse

2.Random error due to human factors. Theoretically, a point captured in two different photos is enough to set its 3D coordinates. To complete this, this step requires an identification and marking of the point in the two images. Any human can have failures in the marking of points, giving rise to the random error.

## **4. Evolution from analytical to digital photogrammetry**

From the analytical photogrammetry, it is possible to describe the evolution from photogrammetry to digital, based on physical and mathematical principles. The main distinction is given by the nature of the measurement of the information taken in the images [15].

The analytical photogrammetry coordinates the image, and the gray digital image is evaluated with the digital photogrammetry. In both methods appropriate Gaussian-Markov evaluation procedures are used. Pertinent relations between object space models and image space data are obtainable. Radiometric concerns take a more important role than previously. The data evaluation of the gray value of the digital image is no longer based on the digital image correlation. As an alternative, the gray values of an image are projected directly onto the models in the object space, this being a new principle. However, these numerical procedures in digital photogrammetry need to be stabilized by adjustment methods. Thus, the original concept of digital photogrammetry can be pragmatic to images from any sensor.

Considerable advances in digital photogrammetry have been made in recent years due to the availability of new hardware and software, such as image processing workstations and increased storage capacity [16, 17].

## **5. Device and acquisition characteristics**

The main camera and photography parameters are focal length, focal point, bias, distortion, and pixel error; they will allow more accurate calibration [18] and are shown in **Figure 3**.

camera are known, any spatial point can be fixed by the intersection of two beams

There are two main factors that induce photogrammetry measurement errors:

1.System error due to lens distortion. It causes a point in the image in the plane to move from its true position (*x, y*) to a disturbed position. The coordinates of

In the lens, the largest error occurs at the point of the projected image. There-

2.Random error due to human factors. Theoretically, a point captured in two different photos is enough to set its 3D coordinates. To complete this, this step requires an identification and marking of the point in the two images. Any human can have failures in the marking of points, giving rise to the random error.

From the analytical photogrammetry, it is possible to describe the evolution from photogrammetry to digital, based on physical and mathematical principles. The main distinction is given by the nature of the measurement of the information

The analytical photogrammetry coordinates the image, and the gray digital image is evaluated with the digital photogrammetry. In both methods appropriate Gaussian-Markov evaluation procedures are used. Pertinent relations between object space models and image space data are obtainable. Radiometric concerns take a more important role than previously. The data evaluation of the gray value of the digital image is no longer based on the digital image correlation. As an alternative, the gray values of an image are projected directly onto the models in the object space, this being a new principle. However, these numerical procedures in digital photogrammetry need to be stabilized by adjustment methods. Thus, the original concept of digital photogrammetry can be pragmatic to images from any sensor. Considerable advances in digital photogrammetry have been made in recent

years due to the availability of new hardware and software, such as image

The main camera and photography parameters are focal length, focal point, bias, distortion, and pixel error; they will allow more accurate calibration [18] and are

processing workstations and increased storage capacity [16, 17].

**5. Device and acquisition characteristics**

**4. Evolution from analytical to digital photogrammetry**

*x*´*<sup>n</sup>* ¼ *xn* þ *dx* (13) *y*´*<sup>n</sup>* ¼ *yn* þ *dy* (14)

*dx* ¼ *dxr* þ *dxd* (15) *dy* ¼ *dyr* þ *dyd* (16)

System error due to lens distortion and random error due to human factors.

any point in the image can be compensated with Eqs. (13)–(14):

fore, *dx*, *dy* can be broken down by Eqs. (15)–(16):

of light that are projected.

*Product Design*

taken in the images [15].

shown in **Figure 3**.

**44**

**Figure 3.** *Scheme of operation of a camera objective.*

## **5.1 Camera objective**

Included in the optical part of the camera, it is in charge in projecting the image that crosses it on the same plane and in outstanding conditions of sharpness. Therefore, it is a matter of focusing on the objects that are at equal distance on the focal plane. From certain distance, all the objects will be projected on the same plane. The light points are transmitted to an element that composes the scenario. As a result of diffraction, this is shown as a circular point with a halo around it and concentric rings, named Airy discs. Suppressing them is unfeasible because it is a physical light effect. Even so, it would be desirable for such rings to be as diffuse and thin as possible [17, 19].

Its resolving capacity depends on two parameters: aberrations and diffraction. One of the main functions of the objective is to suppress aberrations. When the diaphragm is closed, the aberrations are placated, and the only limiting factor is diffraction. When the diaphragm is opened, diffraction diminishes its significance in the wake of aberrations, which add up to force [20].

## **5.2 Focal length**

This parameter is measured from the optical center of the lens to the focal plane, when the camera focused toward the infinity [5, 21]. Normal lenses are those which have a distance close to the diagonal of the cliché. The representation of the focal length is shown in **Figure 4**.

## **5.3 Relative aperture**

Relative aperture (*Ab*) is the connection of the lens diameter (*D*) and its focal length (*f*) (Eq. (17)).

It is shown by the denominator, known as brightness or "f-number." In a different way, the aperture is the span through which light enters to be captured by the sensor. The more spacious the opening will be, the more light will enter the sensor as the number becomes smaller [4, 7]:

$$Ab = \frac{\mathcal{D}}{\mathcal{f}}\tag{17}$$

aperture, and under some pretexts, large aperture systems have more pronounced depths of focus than low aperture systems, even if the depth of field is small [19].

Depth of field is the area of sharp reproduction seen in the photograph. In this one, there are some objects observed which are located at a certain distance, as well

Its function is to modify the light received in order to obtain a digital systematization. The sensor is called pixel in its minimum element. A digital image consists of a set of pixels. The technology based on complementary metal oxide semiconductor (CMOS) sensors is the most applied. The sensors consist of a semiconductor and sensitive material in the visible spectrum, between 300 and 1000 nm [10]. Chargecoupled device (CCD) sensors are becoming obsolete due to the cost and speed of

The comparison reading of the information in the CMOS sensors has the advan-

tage of obtaining enough captures, obtaining readings using less time and with greater flexibility. Using a high dynamic range of work, high contrasts and a correct display of objects are achieved. In terms of quality, the physical size of the sensor is more significant than the number of cells or resolution. A large unit may allow higher-quality photographs to be taken than another sensor with a higher resolution

As far as color is concerned, it must be seen that color is just a human visual perception. In order to be able to glimpse the color of an object, it is necessary to have a light source and something that reflects this light. A color is represented in digital format by applying a system of representation. The most commonly used is the RGB system. To represent a color, the exact percentages of primary red, primary green, and primary blue (RGB, red, green, blue) must be available. By this way, the color is displayed through the implementation of three numbers [24].

The function of this element is to enlarge or decrease the percentage of light circulating through the target. The diaphragm aperture is related to the percentage of aperture it has. It is counted in f-numbers. The step is the shift from one value to the next. The ratio of luminosity, according to the scale of the f, does it in a

The first step in taking a picture is focusing. The most commonly used types of

photodiodes through the sensor. The focusing element is moved in the lens to

• Phase detection autofocus (PDAF). Its management is done by applying

focus the image. It is a slow and inaccurate system due to the use of

**5.7 Depth of field**

processing images.

**5.9 Diaphragm**

*5.10.1 Focus*

**47**

factor of 2 [5] (**Figure 6**).

automatic focusing are [25]:

photodiodes.

**5.10 Other aspects to be taken into account**

but with a smaller surface [23].

**5.8 Sensor**

as others more distant or adjacent to them [20].

*Photogrammetry as an Engineering Design Tool DOI: http://dx.doi.org/10.5772/intechopen.92998*

## **5.4 Field angle**

This is the viewing angle of the camera and is closely related to the focal length and dimension of the sensor [8, 22]. A schematic representation is proposed in **Figure 5**.

## **5.5 Shutter**

It is a mechanism that keeps the light passing through the lens into the closed camera. At certain intervals of time, it has the ability to open, allowing the passage of light so that the film can be impressed. The opening time can be set [21].

## **5.6 Focus depth**

It is related to the permissiveness that occurred between obtaining a sharp image with a suitable impression and another less adequate exposure, although also producing a sharp image. Depth of focus is altered by lens magnification and numerical

**Figure 4.** *Representation and focal length types on a camera.*

## **Figure 5.**

*Focal distances and corresponding angles.*

aperture, and under some pretexts, large aperture systems have more pronounced depths of focus than low aperture systems, even if the depth of field is small [19].

## **5.7 Depth of field**

Depth of field is the area of sharp reproduction seen in the photograph. In this one, there are some objects observed which are located at a certain distance, as well as others more distant or adjacent to them [20].

## **5.8 Sensor**

*Ab* <sup>¼</sup> <sup>D</sup>

This is the viewing angle of the camera and is closely related to the focal length and dimension of the sensor [8, 22]. A schematic representation is proposed in

It is a mechanism that keeps the light passing through the lens into the closed camera. At certain intervals of time, it has the ability to open, allowing the passage of light so that the film can be impressed. The opening time can be set [21].

It is related to the permissiveness that occurred between obtaining a sharp image with a suitable impression and another less adequate exposure, although also producing a sharp image. Depth of focus is altered by lens magnification and numerical

**5.4 Field angle**

*Product Design*

**Figure 5**.

**5.5 Shutter**

**5.6 Focus depth**

**Figure 4.**

**Figure 5.**

**46**

*Focal distances and corresponding angles.*

*Representation and focal length types on a camera.*

<sup>f</sup> (17)

Its function is to modify the light received in order to obtain a digital systematization. The sensor is called pixel in its minimum element. A digital image consists of a set of pixels. The technology based on complementary metal oxide semiconductor (CMOS) sensors is the most applied. The sensors consist of a semiconductor and sensitive material in the visible spectrum, between 300 and 1000 nm [10]. Chargecoupled device (CCD) sensors are becoming obsolete due to the cost and speed of processing images.

The comparison reading of the information in the CMOS sensors has the advantage of obtaining enough captures, obtaining readings using less time and with greater flexibility. Using a high dynamic range of work, high contrasts and a correct display of objects are achieved. In terms of quality, the physical size of the sensor is more significant than the number of cells or resolution. A large unit may allow higher-quality photographs to be taken than another sensor with a higher resolution but with a smaller surface [23].

As far as color is concerned, it must be seen that color is just a human visual perception. In order to be able to glimpse the color of an object, it is necessary to have a light source and something that reflects this light. A color is represented in digital format by applying a system of representation. The most commonly used is the RGB system. To represent a color, the exact percentages of primary red, primary green, and primary blue (RGB, red, green, blue) must be available. By this way, the color is displayed through the implementation of three numbers [24].

## **5.9 Diaphragm**

The function of this element is to enlarge or decrease the percentage of light circulating through the target. The diaphragm aperture is related to the percentage of aperture it has. It is counted in f-numbers. The step is the shift from one value to the next. The ratio of luminosity, according to the scale of the f, does it in a factor of 2 [5] (**Figure 6**).

## **5.10 Other aspects to be taken into account**

## *5.10.1 Focus*

The first step in taking a picture is focusing. The most commonly used types of automatic focusing are [25]:

• Phase detection autofocus (PDAF). Its management is done by applying photodiodes through the sensor. The focusing element is moved in the lens to focus the image. It is a slow and inaccurate system due to the use of photodiodes.


## *5.10.2 Perspective*

A photograph is a perspective image of an object. If straight lines are drawn from all points of an object to a fixed point (called point of view or center of projection) and lines are considered that cross an intermediate surface (called projection surface), the image is drawn on this surface and is known as perspective [1, 26].

The camera is responsible for executing and materializing perspectives of objects. The projection surface is the flat extension of the image sensor or the capture surface. Focal distance is the orthogonal distance separating the viewpoint from the projection surface. Knowing the distance between the point of view and the plane that contains the points of the object, the focal distance with which the photograph was taken and the inclination of the plane in which the points of the object to be measured are located with respect to the projection plane, the reliable coordinates of the points can be disintegrated, using basic trigonometry (**Figure 7**).

The orthogonal and the geometric perspectives are the most widely used in photogrammetry. Using a conventional camera (reel or digital), a geometric perspective will be plotted. From a photograph in which the points of the object to be measured are in a plane parallel to the projection plane or the one on which the photographic film is spread, the real position of the points in space is obtained by using Eqs. (18)–(19):

$$\frac{-\mathbf{x}}{\mathbf{X}} = \frac{\mathbf{X}}{\mathbf{Z}}\tag{18}$$

�y <sup>f</sup> <sup>¼</sup> <sup>Y</sup>

*(x,y)* are the coordinates on the projection plane of the image or photograph.

usually taken in a way that the planes are parallel.

(sensor) and achieve an adequate exposure [9]:

higher the light, the lower the ISO.

*5.10.3 Exposure*

**Figure 7.**

*Diagram of the projection of a camera.*

*Photogrammetry as an Engineering Design Tool DOI: http://dx.doi.org/10.5772/intechopen.92998*

*5.10.4 Dynamic range*

**49**

where *f* is the focal length, (*X, Y, Z*) are the actual coordinates of the point, and *P*

It would be in front of more complex expressions if the planes that contain the points are not parallel to the one of projection, being indispensable to know the inclination of the plane having as reference the plane of projection. In practice, in order to avoid complications in the calculation of coordinates, photographs are

It is based on the capture of a scene by means of a sensitive material. In analog photography, this corresponds to the film and in digital photography, the sensor. Exposure is based on three variables to control the entry of light into the focal plane

1. ISO Sensitivity: it indicates the amount of light required to take a picture. The

2.Diaphragm opening: it inspects the light reaching the focal plane, along with the shutter speed, and regulates the depth of field of the photograph.

3. Shutter speed: shutter opening time allows light to reach the sensor. The higher the shutter speed, the lower the percentage of light reaching the sensor.

When a sensor has the ability to capture as many tones (dynamic range) and

It measures the amount of light and dark tones that a camera has the ability to capture in the same picture. It shows the amount of tonal nuances that a camera is

Contrast and sharpness are based on the differentiation of tonality with which a pair of white and black lines are obtained, captured, or reproduced. It is measurable of the degree of detail, being 100% when both lines can be perfectly differentiated as pure whites and blacks. Resolution and contrast are closely related concepts.

information (light) as its ability allows, the picture is perfectly exposed.

capable of capturing, measurable by contrast and sharpness.

<sup>Z</sup> (19)

## **Figure 6.**

*Solution to (a) different openings, (b) shutter speeds, and (c) ISO.*

*Photogrammetry as an Engineering Design Tool DOI: http://dx.doi.org/10.5772/intechopen.92998*

**Figure 7.** *Diagram of the projection of a camera.*

• Dual pixel. This method uses more focus points along the sensor than the PDAF. This system uses two photodiodes at each pixel to compare minimal

• Contrast detection. It is the oldest of the three systems exposed. Its operation theoretically bases that the contrast of an image is greater, and its edges are appreciated in a clearer way, when it is focused correctly. The disadvantage is

A photograph is a perspective image of an object. If straight lines are drawn from all points of an object to a fixed point (called point of view or center of projection) and lines are considered that cross an intermediate surface (called projection surface), the image is drawn on this surface and is known as perspective [1, 26]. The camera is responsible for executing and materializing perspectives of objects. The projection surface is the flat extension of the image sensor or the capture surface. Focal distance is the orthogonal distance separating the viewpoint from the projection surface. Knowing the distance between the point of view and the plane that contains the points of the object, the focal distance with which the photograph was taken and the inclination of the plane in which the points of the object to be measured are located with respect to the projection plane, the reliable coordinates of the points can be disintegrated, using basic trigonometry (**Figure 7**). The orthogonal and the geometric perspectives are the most widely used in photogrammetry. Using a conventional camera (reel or digital), a geometric perspective will be plotted. From a photograph in which the points of the object to be measured are in a plane parallel to the projection plane or the one on which the photographic film is spread, the real position of the points in space is obtained by

> �x <sup>X</sup> <sup>¼</sup> <sup>X</sup>

<sup>Z</sup> (18)

dissimilarities. This is the most effective focusing technology.

its slowness.

using Eqs. (18)–(19):

**Figure 6.**

**48**

*Solution to (a) different openings, (b) shutter speeds, and (c) ISO.*

*5.10.2 Perspective*

*Product Design*

$$\frac{-\mathbf{y}}{\mathbf{f}} = \frac{\mathbf{Y}}{\mathbf{Z}}\tag{19}$$

where *f* is the focal length, (*X, Y, Z*) are the actual coordinates of the point, and *P (x,y)* are the coordinates on the projection plane of the image or photograph.

It would be in front of more complex expressions if the planes that contain the points are not parallel to the one of projection, being indispensable to know the inclination of the plane having as reference the plane of projection. In practice, in order to avoid complications in the calculation of coordinates, photographs are usually taken in a way that the planes are parallel.

## *5.10.3 Exposure*

It is based on the capture of a scene by means of a sensitive material. In analog photography, this corresponds to the film and in digital photography, the sensor. Exposure is based on three variables to control the entry of light into the focal plane (sensor) and achieve an adequate exposure [9]:


When a sensor has the ability to capture as many tones (dynamic range) and information (light) as its ability allows, the picture is perfectly exposed.

## *5.10.4 Dynamic range*

It measures the amount of light and dark tones that a camera has the ability to capture in the same picture. It shows the amount of tonal nuances that a camera is capable of capturing, measurable by contrast and sharpness.

Contrast and sharpness are based on the differentiation of tonality with which a pair of white and black lines are obtained, captured, or reproduced. It is measurable of the degree of detail, being 100% when both lines can be perfectly differentiated as pure whites and blacks. Resolution and contrast are closely related concepts.

*5.10.6 Environmental conditions*

*Photogrammetry as an Engineering Design Tool DOI: http://dx.doi.org/10.5772/intechopen.92998*

least two or more sensors [26, 29].

characteristics that define it:

*5.10.7 Image quality*

(filtering).

**51**

*5.10.8 Histogram*

Stability of environmental conditions must be achieved:

1.Temperature: the ideal temperature for taking a photograph should be between approximately 18 and 26° in order to avoid dilatation of the lens.

3. Illumination: sufficient light bulb. In most cases, natural light is not sufficient,

Other significant parameters, such as the texture of the element, significantly help the quality of the 3D reconstruction, and optimal results are obtained with the highest level of ambient light (exposure 1/60, f/2.8, and ISO sensitivity 100). The surface of an element should be opaque, with Lambertian reflection and surface homogeneity. A single point on the surface of the object must be visible from at

Image quality is a prerequisite for working with it properly. There are two main

Image processing is the transformation of an input image into an output image.

This is a visual tool very useful for the study of digital images. With the naked eye, it is possible to study the contrast or the distribution of intensities, because it

where *x* is the level of gray or color and *n* is the number of pixels in the image with this value. The histogram is normalized in values ranging from 0 to 1. In

The most common errors in the image, which prevent good image quality, can be identified in the histogram and are muted tones, black areas, overexposure or burned areas, and backlight. In order to know that a good image is acquired, the best thing is to have a histogram that has the shape of a Gauss bell, that is to say, that has the most information in the central part and less in the extremes. Another important point is that the histogram must embrace and reach both ends, so as to

*f x*ð Þ¼*<sup>i</sup> ni* (20)

It is carried out to facilitate the analysis of the image and to obtain a greater reliability of this [30]. Among the transformations, those that eliminate noise or variation in the intensity of the pixels stand out. There are two types of operations: individual operations (rectification or binarization) and neighborhood operations

1.Resolution in amplitude (bit depth): number of bits per point of an image

2. Spatial resolution: the number of pixels per unit area

follows the following discrete function of Eq. (20):

**Figure 9** it is possible to see their different zones [18, 31].

ensure that there are blacks and whites in the photograph.

2.Wind: calm wind, to avoid hindrances when taking the photo.

and it is necessary to use spotlights or other artificial elements.

**Figure 8.** *Contrast sensitivity change as a function of the spatial frequency of the target.*

If the contrast falls below 5%, it is difficult to observe any detail, which is shown more clearly and distinctly the higher it is. Frequency and modulation are shown in the way they are altered when light passes through the different optical components of the lens of the photographed image, thanks to contrast transfer functions. As the viewer moves away, a substantial loss of contrast begins to be noticed [12].

By performing a contrast correction, different filters are applied to the central zones instead of the peripheral zones. An example of contrast and resolution is shown in **Figure 8**.

## *5.10.5 Aberrations*

One of the most outstanding components of a camera is the photographic lens, which produces a series of aberrations that distort the images of the photographs, making difficult to visualize the correct dimensions of the object [27, 28]. There are different types of aberrations, being the most common in photographic lenses:

	- Field curvature: defect when creating the image, being curved instead of flat. It is difficult to correct the aberration, but it can be mitigated in a low percentage.
	- Distortion: only affects the shape of the image. It occurs due to the difference in the scale of reproduction of the image off-axis. If an object with straight lines is photographed, such as a square, the center lines will appear straight, and the edge lines will curve inward or outward producing the so-called barrel or cushion distortions. This aberration is not corrected by closing the diaphragm. This error affects the tone of the image and needs to be corrected.

## *5.10.6 Environmental conditions*

Stability of environmental conditions must be achieved:


Other significant parameters, such as the texture of the element, significantly help the quality of the 3D reconstruction, and optimal results are obtained with the highest level of ambient light (exposure 1/60, f/2.8, and ISO sensitivity 100). The surface of an element should be opaque, with Lambertian reflection and surface homogeneity. A single point on the surface of the object must be visible from at least two or more sensors [26, 29].

## *5.10.7 Image quality*

If the contrast falls below 5%, it is difficult to observe any detail, which is shown more clearly and distinctly the higher it is. Frequency and modulation are shown in the way they are altered when light passes through the different optical components of the lens of the photographed image, thanks to contrast transfer functions. As the

By performing a contrast correction, different filters are applied to the central zones instead of the peripheral zones. An example of contrast and resolution is

One of the most outstanding components of a camera is the photographic lens, which produces a series of aberrations that distort the images of the photographs, making difficult to visualize the correct dimensions of the object [27, 28]. There are different types of aberrations, being the most common in photographic lenses:

1.Point aberrations: housed in the position arranged by the paraxial optics. It is a "stain" instead of a point. There are also chromatic aberration, spherical

2. Shape aberrations: the point is shown as a point but with a different position to the one arranged by means of paraxial approximation. This is a systematic

• Field curvature: defect when creating the image, being curved instead of flat. It is difficult to correct the aberration, but it can be mitigated in a low

difference in the scale of reproduction of the image off-axis. If an object with straight lines is photographed, such as a square, the center lines will appear straight, and the edge lines will curve inward or outward producing the so-called barrel or cushion distortions. This aberration is not corrected by closing the diaphragm. This error affects the tone of the image and needs

• Distortion: only affects the shape of the image. It occurs due to the

error and can be of two types: field curvature and distortions.

viewer moves away, a substantial loss of contrast begins to be noticed [12].

*Contrast sensitivity change as a function of the spatial frequency of the target.*

shown in **Figure 8**.

**Figure 8.**

*Product Design*

*5.10.5 Aberrations*

aberration, astigmatism, and coma.

percentage.

to be corrected.

**50**

Image quality is a prerequisite for working with it properly. There are two main characteristics that define it:

1.Resolution in amplitude (bit depth): number of bits per point of an image

2. Spatial resolution: the number of pixels per unit area

Image processing is the transformation of an input image into an output image. It is carried out to facilitate the analysis of the image and to obtain a greater reliability of this [30]. Among the transformations, those that eliminate noise or variation in the intensity of the pixels stand out. There are two types of operations: individual operations (rectification or binarization) and neighborhood operations (filtering).

## *5.10.8 Histogram*

This is a visual tool very useful for the study of digital images. With the naked eye, it is possible to study the contrast or the distribution of intensities, because it follows the following discrete function of Eq. (20):

$$f(\mathbf{x}\_i) = \mathbf{n}\_i \tag{20}$$

where *x* is the level of gray or color and *n* is the number of pixels in the image with this value. The histogram is normalized in values ranging from 0 to 1. In **Figure 9** it is possible to see their different zones [18, 31].

The most common errors in the image, which prevent good image quality, can be identified in the histogram and are muted tones, black areas, overexposure or burned areas, and backlight. In order to know that a good image is acquired, the best thing is to have a histogram that has the shape of a Gauss bell, that is to say, that has the most information in the central part and less in the extremes. Another important point is that the histogram must embrace and reach both ends, so as to ensure that there are blacks and whites in the photograph.

**Figure 9.** *Histogram areas.*

## *5.10.9 Binarization*

The representation of an image with two values is obtained. The dimensions of the image are still preserved. The decision threshold must be chosen correctly and used in a step filter with an algorithm similar to Eq. (21):

$$\text{g}(\mathbf{x}, \mathbf{y}) = \begin{cases} \text{0} & f(\mathbf{x}, \mathbf{y}) > k \\ \text{1} & f(\mathbf{x}, \mathbf{y}) \le k \end{cases} \tag{21}$$

the value of a pixel p into the position (*x,y*), always taking into account the values of the adjacent pixels. For this operation, a weighted sum of the values of the neighboring points of this point p is required. A mask (*h*), behaving like a filter, is in charge of exposing the values of the weighting. The size of the mask varies

These operations modify the spatial coordinates of the image. There are several operations that are easy to understand and apply, such as interpolation, rotation,

Due to the geometry of the lens, it reproduces a square object with variations in its parallel lines. There are three types of distortion: barrel, pincushion, and mustache (combination of the first two) (**Figure 11**) [25, 33]. This error is negligible in a photograph of a natural scene, but to take engineering measurements and obtain a virtual object, it is necessary to compensate for the distortion. There is a mathe-

The barrel distortion is centered and symmetrical. Therefore, to correct the distortion of a certain point, a radial transformation is performed, expressed math-

ð Þ *x* � *xd*

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

where ð Þ *x*^, ^*y* represents the result of the distortion correction at point (*x, y*), (xd, yd) represents the center of the distortion which is usually a point near the center of the image, and finally the radial function *L(r)* determines the magnitude of the distortion correction as a function of the distance from the point to the center of

The radial function *L(r)* is performed by applying two strategies. The first one

<sup>2</sup> <sup>þ</sup> *<sup>k</sup>*2*<sup>r</sup>*

<sup>2</sup> <sup>þ</sup> *<sup>y</sup>* � *yd* � �<sup>2</sup> q *x* � *xd*

<sup>4</sup> <sup>þ</sup> … <sup>þ</sup> *knr*

<sup>1</sup> <sup>þ</sup> *<sup>k</sup>*1*r*<sup>2</sup> <sup>þ</sup> *<sup>k</sup>*2*r*<sup>4</sup> <sup>þ</sup> … <sup>þ</sup> *knr*<sup>2</sup>*<sup>n</sup>* (24)

*y* � *yd*

� � (22)

<sup>2</sup>*<sup>n</sup>* (23)

according to the pixels used.

**Figure 11.**

*Types of lens distortion.*

*5.10.12 Lens distortion*

ematically in Eq. (22):

distortion [34].

**53**

*5.10.11 Geometrical transformations*

rectification, and distortion correction.

*Photogrammetry as an Engineering Design Tool DOI: http://dx.doi.org/10.5772/intechopen.92998*

matical model for the treatment of distortion.

*x*^ � *xd* ^*y* � *yd* � �

¼ *L*

gives rise to the so-called polynomial models (Eq. (23)):

The second one is based on an approach (Eq. (24)):

*L r*ð Þ¼ 1 þ *k*1*r*

*L r*ð Þ¼ <sup>1</sup>

where 0/1 represents the black/white values and *f* is the value of the gray tone of the coordinates (*x,y*) [32]. **Figure 10** shows a grayscale image versus a binary.

To obtain an image with sufficient quality, the binarization must correspond with white pixels to the objects of interest, being the blacks of the environment. If the object of interest turns out to be darker than the environment, a reversal is applied after the binarization. The most important point in the process is the calculation of the threshold. There are different methods for this: histogram, clustering, entropy, similarity, spatial, global, and local.

The setting of the threshold value is latent, due to its difficulty, in all methods. The techniques are supported by statistics applied to the histogram. They are as follows: carry error method, Otsu method, and Saulova's pixel deviation method.

## *5.10.10 Spatial filtering*

It is based on a convolution operation between the two-dimensional functions image, f, and a nucleus, called h, in digital images. This operation aims to transform

**Figure 10.** *Grayscale (left) and binary (right).*

*Photogrammetry as an Engineering Design Tool DOI: http://dx.doi.org/10.5772/intechopen.92998*

*Types of lens distortion.*

*5.10.9 Binarization*

**Figure 9.** *Histogram areas.*

*Product Design*

*5.10.10 Spatial filtering*

**Figure 10.**

**52**

*Grayscale (left) and binary (right).*

The representation of an image with two values is obtained. The dimensions of the image are still preserved. The decision threshold must be chosen correctly and

> 0 1

where 0/1 represents the black/white values and *f* is the value of the gray tone of

The setting of the threshold value is latent, due to its difficulty, in all methods. The techniques are supported by statistics applied to the histogram. They are as follows: carry error method, Otsu method, and Saulova's pixel deviation method.

It is based on a convolution operation between the two-dimensional functions image, f, and a nucleus, called h, in digital images. This operation aims to transform

*f x*ð Þ , *y* > *k*

*f x*ð Þ , *<sup>y</sup>* <sup>≤</sup>*<sup>k</sup>* (21)

(

the coordinates (*x,y*) [32]. **Figure 10** shows a grayscale image versus a binary. To obtain an image with sufficient quality, the binarization must correspond with white pixels to the objects of interest, being the blacks of the environment. If the object of interest turns out to be darker than the environment, a reversal is applied after the binarization. The most important point in the process is the calculation of the threshold. There are different methods for this: histogram, clus-

used in a step filter with an algorithm similar to Eq. (21):

tering, entropy, similarity, spatial, global, and local.

*g x*ð Þ¼ , *y*

the value of a pixel p into the position (*x,y*), always taking into account the values of the adjacent pixels. For this operation, a weighted sum of the values of the neighboring points of this point p is required. A mask (*h*), behaving like a filter, is in charge of exposing the values of the weighting. The size of the mask varies according to the pixels used.

## *5.10.11 Geometrical transformations*

These operations modify the spatial coordinates of the image. There are several operations that are easy to understand and apply, such as interpolation, rotation, rectification, and distortion correction.

## *5.10.12 Lens distortion*

Due to the geometry of the lens, it reproduces a square object with variations in its parallel lines. There are three types of distortion: barrel, pincushion, and mustache (combination of the first two) (**Figure 11**) [25, 33]. This error is negligible in a photograph of a natural scene, but to take engineering measurements and obtain a virtual object, it is necessary to compensate for the distortion. There is a mathematical model for the treatment of distortion.

The barrel distortion is centered and symmetrical. Therefore, to correct the distortion of a certain point, a radial transformation is performed, expressed mathematically in Eq. (22):

$$
\begin{pmatrix}
\hat{\boldsymbol{\pi}} - \boldsymbol{\pi}\_d \\
\hat{\boldsymbol{\eta}} - \boldsymbol{\eta}\_d
\end{pmatrix} = L\sqrt{(\boldsymbol{\pi} - \boldsymbol{\pi}\_d)^2 + \left(\boldsymbol{\mathcal{y}} - \boldsymbol{\mathcal{y}}\_d\right)^2} \begin{pmatrix}
\boldsymbol{\pi} - \boldsymbol{\pi}\_d \\
\boldsymbol{\eta} - \boldsymbol{\eta}\_d
\end{pmatrix} \tag{22}
$$

where ð Þ *x*^, ^*y* represents the result of the distortion correction at point (*x, y*), (xd, yd) represents the center of the distortion which is usually a point near the center of the image, and finally the radial function *L(r)* determines the magnitude of the distortion correction as a function of the distance from the point to the center of distortion [34].

The radial function *L(r)* is performed by applying two strategies. The first one gives rise to the so-called polynomial models (Eq. (23)):

$$L(r) = \mathbf{1} + k\_1 r^2 + k\_2 r^4 + \dots + k\_n r^{2n} \tag{23}$$

The second one is based on an approach (Eq. (24)):

$$L(r) = \frac{1}{1 + k\_1 r^2 + k\_2 r^4 + \dots + k\_n r^{2n}}\tag{24}$$

The efficiency with which the techniques are used will be linked to the final quality

image pair processing, and model-based sequence processing.

The stereoscopic scene analysis system presented by Koch uses image matching, object segmentation, interpolation, and triangulation techniques to obtain the 3D point density map. The system is divided into three modules: sensor processing,

Pollefeys features a 3D reconstruction process based on well-defined stages. The input is an image sequence, and the output of the process is a 3D surface model. The

Another proposal is expressed by Remondino. He presents a 3D reconstruction

It is used in revolutionary pieces. With only one photograph, it is possible to obtain the axis and dimensions. In 1978 Barrow and Tenenbaum demonstrated that the orientation of the surface along the silhouette can be calculated directly from the image data, resulting in the first study of silhouettes in individual views. Koenderink showed that the sign of the silhouette's curvature is equivalent to that of the Gaussian curvature. Thus, concavities, convexities, and inflections of the silhouette indicate hyperbolic, convex, and parabolic surface points, respectively. Finally, Cipolla and Blake exposed that the curvature of the silhouette has the corresponding sign as the normal curvature along the contour generator in the perspective projection. A similar result was derived for the orthographic projection

First, the silhouette *ρ* of a surface of revolution (SOR) is extracted from the image with a Canny edge detector, and the harmonic homology *W* that maps each side of *ρ* to its symmetrical complement is predictable by minimizing the geometric

The apparent contour is first manually segmented from the rectified silhouette. This can usually be done easily by removing the upper and lower elliptical parts of the silhouette. The points are then sampled from the apparent contour, and the tangent vector (i.e., *x s* \_ð Þ *and y s* \_ ð Þ) at each sample point is calculated by fitting a

For *Ψ* 6¼ *0*, *Rx(Ψ)* first transforms the display vector *p(s)* and the associated surface normal *n(s*) at each sample point: the transformed display vector is normalized so that its third coefficient becomes one, and the following Eqs. (25)–(26) can

*<sup>α</sup>n*ðÞ¼ *<sup>s</sup> p s*ð Þ*<sup>x</sup> dp s*ð Þ

� � � �

�*y s* \_ð Þ *x s* \_ð Þ *x s*ð Þ*y s* \_ðÞ� *x s* \_ð Þ*y s*ð Þ

*ds*

� � � � 3 7

<sup>5</sup> (25)

(26)

detachments among the original silhouette *ρ* and its transformation version *ρ´=Wρ*. The image is rectified, and the axis of the figure is rotated and put in

stages are the following: image ratio, structure and motion recovery, dense

system following these steps: image sequence acquisition and analysis, image calibration and orientation, matching process and the generation of points, and 3D

of the reconstruction.

modeling [18].

by Brady [35].

where

**55**

orthogonal projection (**Figure 13**).

polynomial to the neighboring points.

be used to recover the depth of the sample point:

*n s*ðÞ¼ <sup>1</sup>

*αn*ð Þ*s*

2 6 4

**6.1 From a photograph**

matching, and model construction.

*Photogrammetry as an Engineering Design Tool DOI: http://dx.doi.org/10.5772/intechopen.92998*

**Figure 12.** *Visual example of photo rectification.*

The values *k1–kn* are called distortion model parameters. These values, together with the distortion center coordinates *(xd, yd)*, completely represent the distortion model. The distortion of the lens is represented by the *ki* coefficients. They are obtained from a known calibration image.

## *5.10.13 Rectification (perspective distortion)*

Image correction is necessary because either it is difficult to keep the optical axis vertical at all points of the shot or the axis is tilted toward the vertical. Vertical images are obtained free of displacement because of the inclination of the shot but still have inclinations, product of the depth of the workpiece. Displacements can be suppressed by applying differential grinding or orthorectification process. In the original digital image or a scan, the technique is applied pixel by pixel. In a scanned image, the initial data are the coordinates of the control points. The procedure is divided into two steps:

1.Determination of the mathematical transformation related to real coordinates and those belonging to the image

2.Achievement of new image, being aligned to the reference system

After this process, it is necessary to know that all the pixels of the resulting orthophotography have their level of gray, performing a digital resampling [17, 34]. **Figure 12** shows an unrectified (left) and rectified (right) photograph.

Several resamples are made on the initial image. Three resampling methods are regularly used: bilinear interpolation, nearest neighbor, and bicubic convolution. The transformations to be applied to the images are [19] Helmert transformation; affine transformation; polynomial transformation; and two-dimensional projective transformation.

## **6. Obtaining a 3D model from 2D photographs**

To obtain a 3D model of an object from a 2D one, photographs must be taken from different views, with adequate quality. From these photographs, the reconstruction process begins.

3D reconstruction is the process by which real objects are reproduced on a computer. Nowadays there are several reconstruction techniques and 3D mesh methods, having a function to obtain an algorithm that is able to make the connection of the set of representative points of the object in form of surface elements.

## *Photogrammetry as an Engineering Design Tool DOI: http://dx.doi.org/10.5772/intechopen.92998*

The efficiency with which the techniques are used will be linked to the final quality of the reconstruction.

The stereoscopic scene analysis system presented by Koch uses image matching, object segmentation, interpolation, and triangulation techniques to obtain the 3D point density map. The system is divided into three modules: sensor processing, image pair processing, and model-based sequence processing.

Pollefeys features a 3D reconstruction process based on well-defined stages. The input is an image sequence, and the output of the process is a 3D surface model. The stages are the following: image ratio, structure and motion recovery, dense matching, and model construction.

Another proposal is expressed by Remondino. He presents a 3D reconstruction system following these steps: image sequence acquisition and analysis, image calibration and orientation, matching process and the generation of points, and 3D modeling [18].

## **6.1 From a photograph**

The values *k1–kn* are called distortion model parameters. These values, together with the distortion center coordinates *(xd, yd)*, completely represent the distortion model. The distortion of the lens is represented by the *ki* coefficients. They are

Image correction is necessary because either it is difficult to keep the optical axis

1.Determination of the mathematical transformation related to real coordinates

2.Achievement of new image, being aligned to the reference system

**Figure 12** shows an unrectified (left) and rectified (right) photograph.

**6. Obtaining a 3D model from 2D photographs**

After this process, it is necessary to know that all the pixels of the resulting orthophotography have their level of gray, performing a digital resampling [17, 34].

Several resamples are made on the initial image. Three resampling methods are regularly used: bilinear interpolation, nearest neighbor, and bicubic convolution. The transformations to be applied to the images are [19] Helmert transformation; affine transformation; polynomial transformation; and two-dimensional projective

To obtain a 3D model of an object from a 2D one, photographs must be taken from different views, with adequate quality. From these photographs, the recon-

3D reconstruction is the process by which real objects are reproduced on a computer. Nowadays there are several reconstruction techniques and 3D mesh methods, having a function to obtain an algorithm that is able to make the connection of the set of representative points of the object in form of surface elements.

vertical at all points of the shot or the axis is tilted toward the vertical. Vertical images are obtained free of displacement because of the inclination of the shot but still have inclinations, product of the depth of the workpiece. Displacements can be suppressed by applying differential grinding or orthorectification process. In the original digital image or a scan, the technique is applied pixel by pixel. In a scanned image, the initial data are the coordinates of the control points. The procedure is

obtained from a known calibration image.

*Visual example of photo rectification.*

*5.10.13 Rectification (perspective distortion)*

and those belonging to the image

divided into two steps:

**Figure 12.**

*Product Design*

transformation.

**54**

struction process begins.

It is used in revolutionary pieces. With only one photograph, it is possible to obtain the axis and dimensions. In 1978 Barrow and Tenenbaum demonstrated that the orientation of the surface along the silhouette can be calculated directly from the image data, resulting in the first study of silhouettes in individual views. Koenderink showed that the sign of the silhouette's curvature is equivalent to that of the Gaussian curvature. Thus, concavities, convexities, and inflections of the silhouette indicate hyperbolic, convex, and parabolic surface points, respectively. Finally, Cipolla and Blake exposed that the curvature of the silhouette has the corresponding sign as the normal curvature along the contour generator in the perspective projection. A similar result was derived for the orthographic projection by Brady [35].

First, the silhouette *ρ* of a surface of revolution (SOR) is extracted from the image with a Canny edge detector, and the harmonic homology *W* that maps each side of *ρ* to its symmetrical complement is predictable by minimizing the geometric detachments among the original silhouette *ρ* and its transformation version *ρ´=Wρ*. The image is rectified, and the axis of the figure is rotated and put in orthogonal projection (**Figure 13**).

The apparent contour is first manually segmented from the rectified silhouette. This can usually be done easily by removing the upper and lower elliptical parts of the silhouette. The points are then sampled from the apparent contour, and the tangent vector (i.e., *x s* \_ð Þ *and y s* \_ ð Þ) at each sample point is calculated by fitting a polynomial to the neighboring points.

For *Ψ* 6¼ *0*, *Rx(Ψ)* first transforms the display vector *p(s)* and the associated surface normal *n(s*) at each sample point: the transformed display vector is normalized so that its third coefficient becomes one, and the following Eqs. (25)–(26) can be used to recover the depth of the sample point:

$$m(s) = \frac{1}{a\_n(s)} \begin{bmatrix} -\dot{\boldsymbol{\eta}}(s) \\ \dot{\boldsymbol{\kappa}}(s) \\ \boldsymbol{\kappa}(s)\dot{\boldsymbol{\nu}}(s) - \dot{\boldsymbol{\kappa}}(s)\boldsymbol{\eta}(s) \end{bmatrix} \tag{25}$$

where

$$a\_n(s) = \left| p(s) \varkappa \frac{dp(s)}{ds} \right| \tag{26}$$

The tetrahedron grid can only be used to represent the geometrical structure of geological objects. The natural characteristics of geological objects are reflected in their different attributes, such as different rock formations, different contents of mineral bodies, etc. It is defined that the attribute value of the internal point can be linearly interpolated from the attribute values in four vertices in a tetrahedron. But the attributes could change suddenly between different formations and different mineral bodies. To cope with sudden changes, interpolation of the tetrahedron is needed that can only be applied to six sides of a tetrahedron. Those interpolated

This section presents a robust and precise system for the 3D reconstruction of real objects with shapes and textures in high resolution. The reconstruction method is passive, and the only information required is 2D images obtained with a camera calibrated from different viewing angles as the object rotates on a rotating plate. The triangle surface model is obtained through a scheme that combines the octree construction and the walking cube algorithm. A texture mapping strategy based on surface particles is developed to adequately address photographic-related problems such as inhomogeneous lighting, lights, and occlusion [38]. To conclude, the results of the reconstruction are included to demonstrate the quality obtained (**Figure 15**). The scheme combining octree construction and isolevel extraction through marching cubes is presented for the problem concerning the shape of the silhouette. The use of octree representation allows to reach very high resolutions, while the method of fast walking cubes is adapted through a properly defined isolevel function to work with binary silhouettes, resulting in a mesh of triangles with vertices

Calibration is performed on the camera and rotary table. One of the problems found is the discontinuity of the texture due to the nonhomogeneous lighting in

Next, the octree is represented. An octree is a hierarchical tree structure that can be used to represent volumetric data in terms of cubes of different sizes. Each octree node corresponds to a cube in the octree space that is entirely within the object. This opens up different possibilities: voxels, particles, triangles, and more complicated

points are only used as time data for the following processing [37].

*6.3.2 Reconstruction of objects with high surface and texture resolution*

precisely located in the visual object.

*Photogrammetry as an Engineering Design Tool DOI: http://dx.doi.org/10.5772/intechopen.92998*

**Figure 15.**

**57**

*Flowchart to the reconstruction of objects.*

different parts of the element due to shadows.

**Figure 13.** *Harmonic homology of the figure and its transformation to orthogonal projection [35].*

## **6.2 From two photographs**

This section is based on an investigation using a practical heuristic method, for the reconstruction of structured scenes from two uncalibrated images. The method is based on an initial estimation of the main homographies of the initial 2D point coincidences, which may contain some outliers, and the homographies are recursively refined by incorporating the point and line support coincidences on the main spatial surfaces. The epipolar geometry is then recovered directly from the refined homogenies, and the chambers are calibrated from three orthogonal vanishing points, and the infinite homography is recovered.

First, a simple homography-guided method is proposed to fit and match the line segments between two views, using Canny edge detector and regression algorithms. Second, the cameras are automatically calibrated with the four intrinsic parameters that vary between the two views. A RANSAC mechanism is adopted to detect the main flat surfaces of the object from 2D images. The advantages of the method are that it can build more realistic models with minimal human interactions and it also allows more visible surfaces to be reconstructed on the detected planes than traditional methods that can only reconstruct overlapping parts (**Figure 14**).

## **6.3 Though more than two photographs**

## *6.3.1 Reconstruction of geological objects*

This is one of the fields where photogrammetry is most applied nowadays. In this specific point, the reconstruction is carried out applying Delaunay's triangulation and the tetrahedron. Many data models based on tetrahedron mesh have been developed to represent the complex objects in 3D GIS.

**Figure 14.** *The matching results of the line segments in four main planes [36].*

## *Photogrammetry as an Engineering Design Tool DOI: http://dx.doi.org/10.5772/intechopen.92998*

The tetrahedron grid can only be used to represent the geometrical structure of geological objects. The natural characteristics of geological objects are reflected in their different attributes, such as different rock formations, different contents of mineral bodies, etc. It is defined that the attribute value of the internal point can be linearly interpolated from the attribute values in four vertices in a tetrahedron. But the attributes could change suddenly between different formations and different mineral bodies. To cope with sudden changes, interpolation of the tetrahedron is needed that can only be applied to six sides of a tetrahedron. Those interpolated points are only used as time data for the following processing [37].

## *6.3.2 Reconstruction of objects with high surface and texture resolution*

This section presents a robust and precise system for the 3D reconstruction of real objects with shapes and textures in high resolution. The reconstruction method is passive, and the only information required is 2D images obtained with a camera calibrated from different viewing angles as the object rotates on a rotating plate. The triangle surface model is obtained through a scheme that combines the octree construction and the walking cube algorithm. A texture mapping strategy based on surface particles is developed to adequately address photographic-related problems such as inhomogeneous lighting, lights, and occlusion [38]. To conclude, the results of the reconstruction are included to demonstrate the quality obtained (**Figure 15**).

The scheme combining octree construction and isolevel extraction through marching cubes is presented for the problem concerning the shape of the silhouette. The use of octree representation allows to reach very high resolutions, while the method of fast walking cubes is adapted through a properly defined isolevel function to work with binary silhouettes, resulting in a mesh of triangles with vertices precisely located in the visual object.

Calibration is performed on the camera and rotary table. One of the problems found is the discontinuity of the texture due to the nonhomogeneous lighting in different parts of the element due to shadows.

Next, the octree is represented. An octree is a hierarchical tree structure that can be used to represent volumetric data in terms of cubes of different sizes. Each octree node corresponds to a cube in the octree space that is entirely within the object. This opens up different possibilities: voxels, particles, triangles, and more complicated

**Figure 15.** *Flowchart to the reconstruction of objects.*

**6.2 From two photographs**

**Figure 13.**

*Product Design*

**Figure 14.**

**56**

points, and the infinite homography is recovered.

**6.3 Though more than two photographs**

developed to represent the complex objects in 3D GIS.

*The matching results of the line segments in four main planes [36].*

*6.3.1 Reconstruction of geological objects*

This section is based on an investigation using a practical heuristic method, for the reconstruction of structured scenes from two uncalibrated images. The method is based on an initial estimation of the main homographies of the initial 2D point coincidences, which may contain some outliers, and the homographies are recursively refined by incorporating the point and line support coincidences on the main spatial surfaces. The epipolar geometry is then recovered directly from the refined homogenies, and the chambers are calibrated from three orthogonal vanishing

*Harmonic homology of the figure and its transformation to orthogonal projection [35].*

First, a simple homography-guided method is proposed to fit and match the line segments between two views, using Canny edge detector and regression algorithms. Second, the cameras are automatically calibrated with the four intrinsic parameters that vary between the two views. A RANSAC mechanism is adopted to detect the main flat surfaces of the object from 2D images. The advantages of the method are that it can build more realistic models with minimal human interactions and it also allows more visible surfaces to be reconstructed on the detected planes than tradi-

This is one of the fields where photogrammetry is most applied nowadays. In this specific point, the reconstruction is carried out applying Delaunay's triangulation and the tetrahedron. Many data models based on tetrahedron mesh have been

tional methods that can only reconstruct overlapping parts (**Figure 14**).

**Figure 16.** *From cube to triangulation, adapted from [38].*

parametric primitives, such as splines or NURBS. Voxels are used to represent volumes but can also be used to represent surfaces. A related primitive is a particle that is defined by its color, orientation, and position. By marching the cube triangulation of the octree, the white and black points denote the corners of the cube that are inside and outside, respectively, while the gray points are the points of the triangle's vertex on the surface (**Figure 16**).

approached with a triangular grid, to reduce geometric complexity and adapt the model to the requirements of the computer graphic display system. Then construct a corresponding 3D mesh by placing the triangle vertices in 3D space according to the values found in the corresponding depth map. To reconstruct more complex shapes, the system must combine multiple depth maps. Finally, it is provided with

It is used for medical purposes in many cases, as a base for implants, splints, etc. The process consists of the following parts: acquisition and analysis of the image sequence; calibration and orientation of the images; matching process on the surface of the human body; and generation and modeling of the point cloud. Once the necessary images have been obtained from different points of view, the calibration

The choice of the camera model is often related to the final application and the required accuracy. The correct calibration of the sensor used is one of the main

To evaluate the quality of the matching results, different indicators are used: an ex post standard deviation of the least squares adjustment, the standard deviation of the change in the x-y directions, and the shift from the initial position in the x-y directions. The performance of the process, in the case of uncalibrated images, can

Finally, 3D reconstruction and modeling of the human body shape is performed.

The system is composed of two main modules. The first one is in charge of image

The 3D coordinates of each matching triplet are calculated through a forward intersection. Using collinearity and the results of the orientation process, the 3D paired points are determined with a solution of least squares. For each triplet of images, a point cloud is calculated, and then all the points are joined together to create a unique point cloud. A spatial filter is applied to reduce noise and obtain a more uniform point cloud density. **Figure 18** shows the results before and after filtering (approximately 20,000 points, left); a view of the recovered point cloud

processing, to determine the depth map in a pair of views, where each pair of successive views follows a sequence of phases: detection of points of interest, correspondence of points, and reconstruction of these. In this last phase, the

texture.

**59**

**Figure 17.**

*6.3.4 3D reconstruction of the human body*

*Steps to obtain the 3D model, adapted from [39].*

*Photogrammetry as an Engineering Design Tool DOI: http://dx.doi.org/10.5772/intechopen.92998*

and orientation of the images are carried out.

objectives. Another important point is image matching [40].

with pixel intensity (center); and a 3D human model (right).

only be improved with a local contrast enhancement of the images.

The application of the isolevel function calculated by means of the dichotomous subdivision procedure allows for the construction of a faithful model of the object. The triangular vertices that make up the object's mesh are placed precisely on the surface of the digitized model even at low resolutions. This creates an efficient compromise between resolution and geometric accuracy. The octree construction followed by the walking cube algorithm generates a triangular mesh consisting of an excessive number of triangles, which must be simplified.

## *6.3.3 Object reconstruction*

The reconstruction of objects is mainly based on the archeological field. The process to obtain the 3D model will be governed by **Figure 17**.

First of all, corresponding or common characteristics must be found among the images of the object. The process occurs in two phases:


The system selects two images to set up an initial projective reconstruction frame and then reconstructs the matching feature points through triangulation.

Then a dense surface estimation is performed. To obtain a more detailed model of the observed surface, a dense matching technique is used. The 3D surface is

*Photogrammetry as an Engineering Design Tool DOI: http://dx.doi.org/10.5772/intechopen.92998*

**Figure 17.**

parametric primitives, such as splines or NURBS. Voxels are used to represent volumes but can also be used to represent surfaces. A related primitive is a particle that is defined by its color, orientation, and position. By marching the cube triangulation of the octree, the white and black points denote the corners of the cube that are inside and outside, respectively, while the gray points are the points of the

The application of the isolevel function calculated by means of the dichotomous subdivision procedure allows for the construction of a faithful model of the object. The triangular vertices that make up the object's mesh are placed precisely on the surface of the digitized model even at low resolutions. This creates an efficient compromise between resolution and geometric accuracy. The octree construction followed by the walking cube algorithm generates a triangular mesh consisting of an

The reconstruction of objects is mainly based on the archeological field. The

First of all, corresponding or common characteristics must be found among the

1.The reconstruction algorithm generates a reconstruction in which dimensions

reconstruction equivalent to the original one, formed by a set of 3D points.

2.All the pixels of an image are made to coincide with those of the neighboring

The system selects two images to set up an initial projective reconstruction frame and then reconstructs the matching feature points through triangulation. Then a dense surface estimation is performed. To obtain a more detailed model

of the observed surface, a dense matching technique is used. The 3D surface is

are not correctly defined. A self-calibration algorithm performs a

triangle's vertex on the surface (**Figure 16**).

*From cube to triangulation, adapted from [38].*

*6.3.3 Object reconstruction*

**58**

**Figure 16.**

*Product Design*

excessive number of triangles, which must be simplified.

process to obtain the 3D model will be governed by **Figure 17**.

images so that the system can reconstruct these points.

images of the object. The process occurs in two phases:

*Steps to obtain the 3D model, adapted from [39].*

approached with a triangular grid, to reduce geometric complexity and adapt the model to the requirements of the computer graphic display system. Then construct a corresponding 3D mesh by placing the triangle vertices in 3D space according to the values found in the corresponding depth map. To reconstruct more complex shapes, the system must combine multiple depth maps. Finally, it is provided with texture.

## *6.3.4 3D reconstruction of the human body*

It is used for medical purposes in many cases, as a base for implants, splints, etc. The process consists of the following parts: acquisition and analysis of the image sequence; calibration and orientation of the images; matching process on the surface of the human body; and generation and modeling of the point cloud. Once the necessary images have been obtained from different points of view, the calibration and orientation of the images are carried out.

The choice of the camera model is often related to the final application and the required accuracy. The correct calibration of the sensor used is one of the main objectives. Another important point is image matching [40].

To evaluate the quality of the matching results, different indicators are used: an ex post standard deviation of the least squares adjustment, the standard deviation of the change in the x-y directions, and the shift from the initial position in the x-y directions. The performance of the process, in the case of uncalibrated images, can only be improved with a local contrast enhancement of the images.

Finally, 3D reconstruction and modeling of the human body shape is performed. The 3D coordinates of each matching triplet are calculated through a forward intersection. Using collinearity and the results of the orientation process, the 3D paired points are determined with a solution of least squares. For each triplet of images, a point cloud is calculated, and then all the points are joined together to create a unique point cloud. A spatial filter is applied to reduce noise and obtain a more uniform point cloud density. **Figure 18** shows the results before and after filtering (approximately 20,000 points, left); a view of the recovered point cloud with pixel intensity (center); and a 3D human model (right).

The system is composed of two main modules. The first one is in charge of image processing, to determine the depth map in a pair of views, where each pair of successive views follows a sequence of phases: detection of points of interest, correspondence of points, and reconstruction of these. In this last phase, the

**Conflict of interest**

**Author details**

**61**

Ana Pilar Valerga Puerta<sup>1</sup>

\*, Rocio Aletheia Jimenez-Rodriguez<sup>2</sup>

Sergio Fernandez-Vidal<sup>2</sup> and Severo Raul Fernandez-Vidal<sup>1</sup>

\*Address all correspondence to: anapilar.valerga@uca.es

Engineering, University of Cadiz, Cadiz, Spain

provided the original work is properly cited.

1 Department of Mechanical Engineering and Industrial Design, School of

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

2 Reanimalia. Rehabilitacion and Ortopedia Veterinaria, Cadiz, Spain

,

The authors declare no conflict of interest.

*Photogrammetry as an Engineering Design Tool DOI: http://dx.doi.org/10.5772/intechopen.92998*

**Figure 18.** *3D reconstruction of a human body, adapted from [40].*

parameters that describe the movement (rotation matrix R and translation vector T) between the two views are determined. This sequence of steps is repeated for all successive pairs of views of the set.

The second module is responsible for creating the 3D model, for which it must determine the total 3D points map generated. In each iteration of the previous module, the 3D mesh is generated by applying Delaunay's triangulation method. The results obtained from the process are modeled in a virtual environment to obtain a more realistic visualization of the object [16].

The number of detected minutiae is related to the number of reconstructed 3D points and the quality of that reconstruction (higher number of details). Therefore, the higher the number of points on the map, the more detailed areas are obtained. In some cases this does not apply, due to the geometry of the object, for example, in a cube, more points can result in a distorted object.

## **7. Conclusion**

The technological development of 3D photogrammetry makes it a real option in the various applications of 3D scanners. Among the different benefits it brings are faster raw data acquisition, simplicity, portability, and more economical equipment. Different studies have verified the accuracy and repeatability of 3D photogrammetry. These investigations have compared the digital models of objects obtained from 2D digital photographs with those generated by a 3D surface scanner. In general, the meshes obtained with photogrammetric techniques and with scanners show a low degree of deviation from each other. The surface settings of photogrammetric models are usually a little better. For these reasons, photogrammetry is a technology with an infinite number of engineering applications.

In this chapter the basic fundamentals, the characteristics of the acquisition, and the aspects to be taken into account to obtain a good virtual model from photogrammetry have been explained.

## **Acknowledgements**

The authors would like to thank the call for Innovation and Teaching Improvement Projects of the University of Cadiz and AIRBUS-UCA Innovation Unit (UIC) for the Development of Advanced Manufacturing Technologies in the Aeronautical Industry.

*Photogrammetry as an Engineering Design Tool DOI: http://dx.doi.org/10.5772/intechopen.92998*

## **Conflict of interest**

parameters that describe the movement (rotation matrix R and translation vector T) between the two views are determined. This sequence of steps is repeated for all

The second module is responsible for creating the 3D model, for which it must determine the total 3D points map generated. In each iteration of the previous module, the 3D mesh is generated by applying Delaunay's triangulation method. The results obtained from the process are modeled in a virtual environment to

The number of detected minutiae is related to the number of reconstructed 3D points and the quality of that reconstruction (higher number of details). Therefore, the higher the number of points on the map, the more detailed areas are obtained. In some cases this does not apply, due to the geometry of the object, for example, in a

The technological development of 3D photogrammetry makes it a real option in the various applications of 3D scanners. Among the different benefits it brings are faster raw data acquisition, simplicity, portability, and more economical equipment. Different studies have verified the accuracy and repeatability of 3D photogrammetry. These investigations have compared the digital models of objects obtained from 2D digital photographs with those generated by a 3D surface scanner. In general, the meshes obtained with photogrammetric techniques and with scanners show a low degree of deviation from each other. The surface settings of photogrammetric models are usually a little better. For these reasons, photogrammetry is a technology

In this chapter the basic fundamentals, the characteristics of the acquisition, and the aspects to be taken into account to obtain a good virtual model from photo-

The authors would like to thank the call for Innovation and Teaching Improvement Projects of the University of Cadiz and AIRBUS-UCA Innovation Unit (UIC) for the Development of Advanced Manufacturing Technologies in the

successive pairs of views of the set.

*3D reconstruction of a human body, adapted from [40].*

**7. Conclusion**

**Figure 18.**

*Product Design*

obtain a more realistic visualization of the object [16].

cube, more points can result in a distorted object.

with an infinite number of engineering applications.

grammetry have been explained.

**Acknowledgements**

Aeronautical Industry.

**60**

The authors declare no conflict of interest.

## **Author details**

Ana Pilar Valerga Puerta<sup>1</sup> \*, Rocio Aletheia Jimenez-Rodriguez<sup>2</sup> , Sergio Fernandez-Vidal<sup>2</sup> and Severo Raul Fernandez-Vidal<sup>1</sup>

1 Department of Mechanical Engineering and Industrial Design, School of Engineering, University of Cadiz, Cadiz, Spain

2 Reanimalia. Rehabilitacion and Ortopedia Veterinaria, Cadiz, Spain

\*Address all correspondence to: anapilar.valerga@uca.es

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## **References**

[1] Rolin R, Antaluca E, Batoz JL, Lamarque F, Lejeune M. From point cloud data to structural analysis through a geometrical hBIM-oriented model. Journal of Cultural Heritage. 2019;**12**: 1-26. DOI: 10.1145/3242901

[2] Valerga AP, Batista M, Bienvenido R, Fernández-Vidal SR, Wendt C, Marcos M. Reverse engineering based methodology for modelling cutting tools. Procedia Engineering. 2015;**132**: 1144-1151. DOI: 10.1016/j. proeng.2015.12.607

[3] Rabbani T, Dijkman S, van den Heuvel F, Vosselman G. An integrated approach for modelling and global registration of point clouds. ISPRS Journal of Photogrammetry and Remote Sensing. 2007;**61**:355-370. DOI: 10.1016/ j.isprsjprs.2006.09.006

[4] Schenk T. Introduction to Photogrammetry. Department of Civil and Environmental Engineering and Geodetic Science. Athens, USA: The Ohio State University; 2005. pp. 79-95. Available from: http://gscphoto.ceegs. ohio-state.edu/courses/GeodSci410/ docs/GS410\_02.pdf

[5] Derenyi EE. Photogrammetry: The Concepts. Canada: Department of Geodesy and Geomatics Engineering University of New Brunswick; 1996. DOI: 10.1017/9781108665537.002

[6] Ackermann F. Digital image correlation: Performance and potential application in photogrammetry. The Photogrammetric Record. 1984;**11**: 429-439. DOI: 10.1111/j.1477-9730.1984. tb00505.x

[7] Sansoni G, Trebeschi M, Docchio F. State-of-the-art and applications of 3D imaging sensors in industry, cultural heritage, medicine, and criminal investigation. Sensors. 2009;**9**:568-601. DOI: 10.3390/s90100568

[8] Katz D, Friess M. Technical note: 3D from standard digital photography of human crania - A preliminary assessment. American Journal of Physical Anthropology. 2014;**154**: 152-158. DOI: 10.1002/ajpa.22468

Engineering and Management. 2010; **136**:242-250. DOI: 10.1061/(ASCE)

*Photogrammetry as an Engineering Design Tool DOI: http://dx.doi.org/10.5772/intechopen.92998*

> surface area and three dimensional shape measurement of coral skeletons.

Methods. 2010;**8**:241-253. DOI: 10.4319/

[22] Kaufman J, Clement M, Rennie AE. Reverse engineering using close range

manufactured reproduction of Egyptian

Computing and Information Science in Engineering. 2015;**15**:1-7. DOI: 10.1115/

[23] Bernard A. Reverse engineering for rapid product development: A state of the art. Three-Dimensional Imaging, Optical Metrology, and Inspection. 1999; **3835**:50-63. DOI: 10.1117/12.370268

[24] Adeline KRM, Chen M, Briottet X, Pang SK, Paparoditis N. Shadow detection in very high spatial resolution aerial images: A comparative study. ISPRS Journal of Photogrammetry and Remote Sensing. 2013;**80**:21-38. DOI: 10.1016/j.isprsjprs.2013.02.003

[25] Yi XF, Long SC. Precision displacement measurement of single lens reflex digital camera. Applied Mechanics and Materials. 2011;**103**: 82-86. DOI: 10.4028/www.scientific.

[26] Webster C, Westoby M, Rutter N, Jonas T. Three-dimensional thermal characterization of forest canopies using UAV photogrammetry. Remote Sensing of Environment. 2018;**209**:835-847. DOI: 10.1016/j.rse.2017.09.033

[27] Galantucci LM, Lavecchia F, Percoco G, Raspatelli S. New method to calibrate and validate a high-resolution 3D scanner, based on photogrammetry. Precision Engineering. 2014;**38**:279-291. DOI: 10.1016/j.precisioneng.2013.10.002

[28] Menna F, Nocerino E,

Remondino F. Optical aberrations in

net/AMM.103.82

Limnology and Oceanography:

photogrammetry for additive

artifacts and other Objets d'art (ESDA1014-20304). Journal of

lom.2010.8.241

1.4028960

[15] Wrobel BP. The evolution of digital photogrammetry from analytical

photogrammetry. The Photogrammetric Record. 1991;**13**:765-776. DOI: 10.1111/

Photography-based façade recovery & 3-d modeling: A CAD application in cultural heritage. Journal of Cultural Heritage. 2011;**12**:243-252. DOI: 10.1016/j.culher.2010.12.008

[17] Murtiyoso A, Grussenmeyer P, Börlin N. Reprocessing close range terrestrial and uav photogrammetric projects with the dbat toolbox for independent verification and quality control. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. 2017;**42**:171-177. DOI:

10.5194/isprs-archives-XLII-2-W8-171-

[18] Remondino F, Fraser C. Digital camera calibration methods:

International Archives of the

10.1179/146531205225021687

Considerations and comparisons. The

Photogrammetry, Remote Sensing and Spatial Information Sciences. 2006;**36**:

[19] Bister D, Mordarai F, Aveling RM. Comparison of 10 digital SLR cameras for orthodontic photography. Journal of Orthodontics. 2006;**33**:223-230. DOI:

[20] Bill Triggs AWF, McLauchlan PF, Hartley RI. In: Triggs B, editor. Bundle Adjustment—A Modern Synthesis Bill. Vision Algorithms '99. LNCS 1883; **2000**:298-372. DOI: 10.3760/cma.j. issn.2095-4352.2016.06.018

[21] Veal CJ, Holmes G, Nunez M, Hoegh-Guldberg O, Osborn J. A comparative study of methods for

2017

266-272

**63**

CO.1943-7862.0000114

j.1477-9730.1991.tb00738.x

[16] Styliadis AD, Sechidis LA.

[9] Martorelli M, Lepore A, Lanzotti A. Quality analysis of 3D reconstruction in underwater photogrammetry by bootstrapping design of experiments. International Journal of Mechanical Sciences. 2016;**10**:39-45

[10] Guerra MG, Lavecchia F, Maggipinto G, Galantucci LM, Longo GA. Measuring techniques suitable for verification and repairing of industrial components: A comparison among optical systems. CIRP Journal of Manufacturing Science and Technology. 2019;**27**:114-123. DOI: 10.1016/j. cirpj.2019.09.003

[11] Givi M, Cournoyer L, Reain G, Eves BJ. Performance evaluation of a portable 3D imaging system. Precision Engineering. 2019;**59**:156-165. DOI: 10.1016/j.precisioneng.2019.06.002

[12] Aguilar R, Noel MF, Ramos LF. Integration of reverse engineering and non-linear numerical analysis for the seismic assessment of historical adobe buildings. Automation in Construction. 2019;**98**:1-15. DOI: 10.1016/j. autcon.2018.11.010

[13] Murphy M, McGovern E, Pavia S. Historic building information modelling - Adding intelligence to laser and image based surveys of European classical architecture. ISPRS Journal of Photogrammetry and Remote Sensing. 2013;**76**:89-102. DOI: 10.1016/j. isprsjprs.2012.11.006

[14] Dai F, Lu M. Assessing the accuracy of applying photogrammetry to take geometric measurements on building products. Journal of Construction

*Photogrammetry as an Engineering Design Tool DOI: http://dx.doi.org/10.5772/intechopen.92998*

Engineering and Management. 2010; **136**:242-250. DOI: 10.1061/(ASCE) CO.1943-7862.0000114

**References**

*Product Design*

[1] Rolin R, Antaluca E, Batoz JL, Lamarque F, Lejeune M. From point cloud data to structural analysis through a geometrical hBIM-oriented model. Journal of Cultural Heritage. 2019;**12**:

[8] Katz D, Friess M. Technical note: 3D from standard digital photography of

[9] Martorelli M, Lepore A, Lanzotti A. Quality analysis of 3D reconstruction in underwater photogrammetry by bootstrapping design of experiments. International Journal of Mechanical

human crania - A preliminary assessment. American Journal of Physical Anthropology. 2014;**154**: 152-158. DOI: 10.1002/ajpa.22468

Sciences. 2016;**10**:39-45

[10] Guerra MG, Lavecchia F, Maggipinto G, Galantucci LM, Longo GA. Measuring techniques suitable for verification and repairing of industrial components: A comparison among optical systems. CIRP Journal of Manufacturing Science and Technology.

2019;**27**:114-123. DOI: 10.1016/j.

[11] Givi M, Cournoyer L, Reain G, Eves BJ. Performance evaluation of a portable 3D imaging system. Precision Engineering. 2019;**59**:156-165. DOI: 10.1016/j.precisioneng.2019.06.002

[12] Aguilar R, Noel MF, Ramos LF. Integration of reverse engineering and non-linear numerical analysis for the seismic assessment of historical adobe buildings. Automation in Construction.

[13] Murphy M, McGovern E, Pavia S. Historic building information modelling - Adding intelligence to laser and image based surveys of European classical architecture. ISPRS Journal of

Photogrammetry and Remote Sensing. 2013;**76**:89-102. DOI: 10.1016/j.

[14] Dai F, Lu M. Assessing the accuracy of applying photogrammetry to take geometric measurements on building products. Journal of Construction

2019;**98**:1-15. DOI: 10.1016/j.

autcon.2018.11.010

isprsjprs.2012.11.006

cirpj.2019.09.003

1-26. DOI: 10.1145/3242901

1144-1151. DOI: 10.1016/j. proeng.2015.12.607

j.isprsjprs.2006.09.006

docs/GS410\_02.pdf

tb00505.x

**62**

[4] Schenk T. Introduction to

Photogrammetry. Department of Civil and Environmental Engineering and Geodetic Science. Athens, USA: The Ohio State University; 2005. pp. 79-95. Available from: http://gscphoto.ceegs. ohio-state.edu/courses/GeodSci410/

[5] Derenyi EE. Photogrammetry: The Concepts. Canada: Department of Geodesy and Geomatics Engineering University of New Brunswick; 1996. DOI: 10.1017/9781108665537.002

[6] Ackermann F. Digital image correlation: Performance and potential application in photogrammetry. The Photogrammetric Record. 1984;**11**: 429-439. DOI: 10.1111/j.1477-9730.1984.

[7] Sansoni G, Trebeschi M, Docchio F. State-of-the-art and applications of 3D imaging sensors in industry, cultural heritage, medicine, and criminal investigation. Sensors. 2009;**9**:568-601.

DOI: 10.3390/s90100568

Fernández-Vidal SR, Wendt C, Marcos M. Reverse engineering based methodology for modelling cutting tools. Procedia Engineering. 2015;**132**:

[3] Rabbani T, Dijkman S, van den Heuvel F, Vosselman G. An integrated approach for modelling and global registration of point clouds. ISPRS Journal of Photogrammetry and Remote Sensing. 2007;**61**:355-370. DOI: 10.1016/

[2] Valerga AP, Batista M, Bienvenido R,

[15] Wrobel BP. The evolution of digital photogrammetry from analytical photogrammetry. The Photogrammetric Record. 1991;**13**:765-776. DOI: 10.1111/ j.1477-9730.1991.tb00738.x

[16] Styliadis AD, Sechidis LA. Photography-based façade recovery & 3-d modeling: A CAD application in cultural heritage. Journal of Cultural Heritage. 2011;**12**:243-252. DOI: 10.1016/j.culher.2010.12.008

[17] Murtiyoso A, Grussenmeyer P, Börlin N. Reprocessing close range terrestrial and uav photogrammetric projects with the dbat toolbox for independent verification and quality control. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. 2017;**42**:171-177. DOI: 10.5194/isprs-archives-XLII-2-W8-171- 2017

[18] Remondino F, Fraser C. Digital camera calibration methods: Considerations and comparisons. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2006;**36**: 266-272

[19] Bister D, Mordarai F, Aveling RM. Comparison of 10 digital SLR cameras for orthodontic photography. Journal of Orthodontics. 2006;**33**:223-230. DOI: 10.1179/146531205225021687

[20] Bill Triggs AWF, McLauchlan PF, Hartley RI. In: Triggs B, editor. Bundle Adjustment—A Modern Synthesis Bill. Vision Algorithms '99. LNCS 1883; **2000**:298-372. DOI: 10.3760/cma.j. issn.2095-4352.2016.06.018

[21] Veal CJ, Holmes G, Nunez M, Hoegh-Guldberg O, Osborn J. A comparative study of methods for surface area and three dimensional shape measurement of coral skeletons. Limnology and Oceanography: Methods. 2010;**8**:241-253. DOI: 10.4319/ lom.2010.8.241

[22] Kaufman J, Clement M, Rennie AE. Reverse engineering using close range photogrammetry for additive manufactured reproduction of Egyptian artifacts and other Objets d'art (ESDA1014-20304). Journal of Computing and Information Science in Engineering. 2015;**15**:1-7. DOI: 10.1115/ 1.4028960

[23] Bernard A. Reverse engineering for rapid product development: A state of the art. Three-Dimensional Imaging, Optical Metrology, and Inspection. 1999; **3835**:50-63. DOI: 10.1117/12.370268

[24] Adeline KRM, Chen M, Briottet X, Pang SK, Paparoditis N. Shadow detection in very high spatial resolution aerial images: A comparative study. ISPRS Journal of Photogrammetry and Remote Sensing. 2013;**80**:21-38. DOI: 10.1016/j.isprsjprs.2013.02.003

[25] Yi XF, Long SC. Precision displacement measurement of single lens reflex digital camera. Applied Mechanics and Materials. 2011;**103**: 82-86. DOI: 10.4028/www.scientific. net/AMM.103.82

[26] Webster C, Westoby M, Rutter N, Jonas T. Three-dimensional thermal characterization of forest canopies using UAV photogrammetry. Remote Sensing of Environment. 2018;**209**:835-847. DOI: 10.1016/j.rse.2017.09.033

[27] Galantucci LM, Lavecchia F, Percoco G, Raspatelli S. New method to calibrate and validate a high-resolution 3D scanner, based on photogrammetry. Precision Engineering. 2014;**38**:279-291. DOI: 10.1016/j.precisioneng.2013.10.002

[28] Menna F, Nocerino E, Remondino F. Optical aberrations in underwater photogrammetry with flat and hemispherical dome ports. In: Videometrics, Range Imaging, and Applications XIV. SPIE Optical Metrology. Munich, Germany: SPIE;. 2017;**1033205**:1-14. DOI: 10.1117/ 12.2270765

[29] Nevalainen O, Honkavaara E, Tuominen S, Viljanen N, Hakala T, Yu X, et al. Individual tree detection and classification with UAV-based photogrammetric point clouds and hyperspectral imaging. Remote Sensing. 2017;**9**:1-34. DOI: 10.3390/rs9030185

[30] Yoo Y, Lee S, Choe W, Kim C. CMOS image sensor noise reduction method for image signal processor in digital cameras and camera phones Youngjin. In: Proceedings of SPIE-IS&T Electronic Imaging Digital Photography III. 2007. pp. 1-10. DOI: 10.1117/12.702758

[31] Fliegel K, Havlin J. Imaging photometer with a non-professional digital camera. In: SPIE 7443, Applications of Digital Image Processing XXXII. 2009. pp. 1-8. DOI: 10.1117/12.825977

[32] Gomez-Gil P. Shape-based hand recognition approach using the morphological pattern spectrum. Journal of Electronic Imaging. 2009;**18**: 13012. DOI: 10.1117/1.3099712

[33] Ng R, Hanrahan PM. Digital correction of lens aberrations in light field photography. In: International Optical Design Conference. 2006. p. 6342. DOI: 10.1117/12.692290

[34] Jianping Z, John G. Image pipeline tuning for digital cameras. In: IEEE International Symposium on Consumer Electronics. Irving, TX; 2007. pp. 167-170

[35] Wong KYK, Mendonça PRS, Cipolla R. Reconstruction of surfaces of revolution from single uncalibrated

views. Image and Vision Computing. 2004;**22**:829-836. DOI: 10.1016/j. imavis.2004.02.003

[36] Wang G, Tsui HT, Hu Z. Reconstruction of structured scenes from two uncalibrated images. Pattern Recognition Letters. 2005;**26**:207-220. DOI: 10.1016/j.patrec.2004.08.024

[37] Xue Y, Sun M, Ma A. On the reconstruction of three-dimensional complex geological objects using Delaunay triangulation. Future Generation Computer Systems. 2004; **20**:1227-1234. DOI: 10.1016/j. future.2003.11.012

[38] Yemez Y, Schmitt F. 3D reconstruction of real objects with high resolution shape and texture. Image and Vision Computing. 2004;**22**:1137-1153. DOI: 10.1016/j.imavis.2004.06.001

[39] Pollefeys M, Van Gool L, Vergauwen M, Cornelis K, Verbiest F, Tops J. 3D recording for archaeological fieldwork. IEEE Computer Graphics and Applications. 2003;May/June:20-27. DOI: 10.1109/MCG.2003.1198259

[40] Remondino F. 3-D reconstruction of static human body shape from image sequence. Computer Vision and Image Understanding. 2004;**93**:65-85. DOI: 10.1016/j.cviu.2003.08.006

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Section 2

Product Development

Section 2
