**2. Introducing intelligence to manufacturing functions**

Success of manufacturing systems in industry 4.0 era will be heavily dependent upon their capability to perform operations with a certain degree of autonomy which can be assured by employing artificial intelligence in designing manufacturing systems to some extent. Oztemel (2010) describes a general framework with possible characteristics of intelligent manufacturing systems [8]. Due to wide variety of products and services requiring very complex operations, manufacturing systems are facing so many challenges. For example, customization is a very demanding properties of the products and services for intended customers. There seems to be several unique requirements like this one for the manufacturing systems to cope with the existing transformation need. Some of these are;


**3**

*Introduction to Intelligent Quality Management DOI: http://dx.doi.org/10.5772/intechopen.94971*

manufacturers in the following decade.

intelligent behavior of certain type of quality functions.

**3. The need for integrated manufacturing system**

expected output.

It is now very obvious that generating a required level of flexibility in manufacturing system is not enough for proper transformation. Whole supply chain is to be involved. Unmanned operations and systems, on the other hand, generates more competitiveness and puts more challenges for those which are having lack of knowledge on generating the required autonomy. Since, artificial intelligence, information technology, and robots with well performing sensors are the technologies to form advanced manufacturing systems which are to be capable of overcoming those challenges, manufacturers should spend a great amount of effort to learn and experiment these in their operations. In other words, the manufacturers may need to follow lean principles together with intelligent equipment. This means, they need to optimize productivity and effectiveness of their manufacturing hardware, reduce possible wastes (scraps, overtimes, costs etc.), reduce cycle times and increase the

Intelligent equipment should not only be able to utilize artificial intelligence and machine learning technologies, but also be capable of processing big data collected over a certain amount of sensors in different roles for the sake of sustaining proactive management of equipment, processes services as well as products. Generating intelligent systems in this manner requires utilizing advanced information and manufacturing technologies for sustaining intelligence, re-configurability, interoperability, reusability and flexibility. Shen and Norrie (1999) provided a survey pointing out various requirements of especially agent based intelligent manufacturing systems [9]. Note that intelligent manufacturing, requires certain type of technologies for assuring the manufacturing equipment to behave as much intelligent as possible. Internet of Things, Cyber-physical systems, cloud computing, big data analytics, learning events, generating immediate responses to unexpected changes around etc. will be inevitable to sustain required level of smartness. Note that the production life cycle as a whole should be integrated and motivated by AI enriched sensors, decision making systems with big data analytics as well as advanced materials [10]. It seems that the competitiveness of manufacturing systems will heavily be dependent upon the level of intelligence they possess. Especially, utilizing data integration capabilities of information systems, intelligent decision making and reasoning capability (cognitive evaluation) over the data available, representing the results of the cognition process through dashboards and interactive visual analytics, employing smart sensor technologies so on and so forth will be the main focus of

For a manufacturing system, being able to process real time data gathered from the machines and performing intelligent analysis over those through implementing AI technologies make it to predict and understand critical events and solve problems immediately before yielding any dangerous and hazardous situation as well as wastes. This capability of manufacturing systems even allows the machine to perform predictive maintenance and generate well operating manufacturing suits. Current literature is rich enough to provide information regarding intelligent manufacturing as well as various applications. The reader may not have any difficulty in reaching those. For the purpose of this Chapter, it would be beneficial to mention some of the areas where AI technology is heavily utilized in generating

Since human intervention is minimized in intelligent operations, the systems should be capable of communicating with one another in order to make sure that the full manufacturing cycle is to operate without and delays and problems. This

*Introduction to Intelligent Quality Management DOI: http://dx.doi.org/10.5772/intechopen.94971*

*Quality Control - Intelligent Manufacturing, Robust Design and Charts*

and the range of applications.

the quality of products [4].

self-operating and capable of working independently but coordinating with others in a well-designed manufacturing suits [2]. Their approach contributed to the developed a reference model of fully integrated, intelligent manufacturing system so called REMIMS [3]. Application of artificial intelligence techniques and methodologies to quality domain varies. There are vast amount of research and literature available. Here in this Chapter, only a few of them (that are different nature in a wide spectrum) will be mentioned for indicating the importance of this initiatives

Computer vision technology and artificial intelligence, for example, are well utilized for quality control purpose in many different applications. As the quality of many products in manufacturing is defined by the dimensions and surface features, computer vision technology is utilized for mainly replacing human eyes. This attracted manufacturing community due to cost effectiveness. It is now well proven that together with utilizing statistical analysis methods, automated machine vision systems are capable of analyzing geometric and surface features for deciding about

Similarly, Xu et al. (2018) provided an intelligent quality problem solving system capable of performing a comprehensive analysis of data mining process and methods. They run a pilot application in automotive manufacturing company to demonstrate intelligent capabilities of the system proposed [5]. Bihi et al. (2018) designed and intelligent quality management systems which can learn and adapt itself to the

requirements of flexible manufacturing systems [6]. The goal of this system is to learn line layout and setup through predefined training cycles. Tseng et al. (2016) presented a remote part tracking and quality control system, so called e-quality control, based on the application of support vector machine (SVM) to predict the output quality [7]. Like some of those mentioned above, a comprehensive literature review will provide huge amount of information on implementing artificial intelligence on quality functions. The information stated above is considered to be sufficient enough for taking the attention of the reader to intelligent quality operations. Note that, the remaining part of this chapter will highlight the importance of intelligent tools for sustaining the quality of systems, processes and products. Also note that, the Chapter will describe a general framework for utilizing intelligent quality systems.

Success of manufacturing systems in industry 4.0 era will be heavily dependent

• implementing new methodologies for achieving the required level of transfor-

upon their capability to perform operations with a certain degree of autonomy which can be assured by employing artificial intelligence in designing manufacturing systems to some extent. Oztemel (2010) describes a general framework with possible characteristics of intelligent manufacturing systems [8]. Due to wide variety of products and services requiring very complex operations, manufacturing systems are facing so many challenges. For example, customization is a very demanding properties of the products and services for intended customers. There seems to be several unique requirements like this one for the manufacturing systems

**2. Introducing intelligence to manufacturing functions**

to cope with the existing transformation need. Some of these are;

mation to smartness in the traditional manufacturing systems.

• to be able to adopt new models,

• generating new forms of manufacturing,

**2**

It is now very obvious that generating a required level of flexibility in manufacturing system is not enough for proper transformation. Whole supply chain is to be involved. Unmanned operations and systems, on the other hand, generates more competitiveness and puts more challenges for those which are having lack of knowledge on generating the required autonomy. Since, artificial intelligence, information technology, and robots with well performing sensors are the technologies to form advanced manufacturing systems which are to be capable of overcoming those challenges, manufacturers should spend a great amount of effort to learn and experiment these in their operations. In other words, the manufacturers may need to follow lean principles together with intelligent equipment. This means, they need to optimize productivity and effectiveness of their manufacturing hardware, reduce possible wastes (scraps, overtimes, costs etc.), reduce cycle times and increase the expected output.

Intelligent equipment should not only be able to utilize artificial intelligence and machine learning technologies, but also be capable of processing big data collected over a certain amount of sensors in different roles for the sake of sustaining proactive management of equipment, processes services as well as products. Generating intelligent systems in this manner requires utilizing advanced information and manufacturing technologies for sustaining intelligence, re-configurability, interoperability, reusability and flexibility. Shen and Norrie (1999) provided a survey pointing out various requirements of especially agent based intelligent manufacturing systems [9]. Note that intelligent manufacturing, requires certain type of technologies for assuring the manufacturing equipment to behave as much intelligent as possible. Internet of Things, Cyber-physical systems, cloud computing, big data analytics, learning events, generating immediate responses to unexpected changes around etc. will be inevitable to sustain required level of smartness. Note that the production life cycle as a whole should be integrated and motivated by AI enriched sensors, decision making systems with big data analytics as well as advanced materials [10]. It seems that the competitiveness of manufacturing systems will heavily be dependent upon the level of intelligence they possess. Especially, utilizing data integration capabilities of information systems, intelligent decision making and reasoning capability (cognitive evaluation) over the data available, representing the results of the cognition process through dashboards and interactive visual analytics, employing smart sensor technologies so on and so forth will be the main focus of manufacturers in the following decade.

For a manufacturing system, being able to process real time data gathered from the machines and performing intelligent analysis over those through implementing AI technologies make it to predict and understand critical events and solve problems immediately before yielding any dangerous and hazardous situation as well as wastes. This capability of manufacturing systems even allows the machine to perform predictive maintenance and generate well operating manufacturing suits.

Current literature is rich enough to provide information regarding intelligent manufacturing as well as various applications. The reader may not have any difficulty in reaching those. For the purpose of this Chapter, it would be beneficial to mention some of the areas where AI technology is heavily utilized in generating intelligent behavior of certain type of quality functions.

#### **3. The need for integrated manufacturing system**

Since human intervention is minimized in intelligent operations, the systems should be capable of communicating with one another in order to make sure that the full manufacturing cycle is to operate without and delays and problems. This

implies that manufacturing systems should employ knowledge protocols or any other knowledge transfer language [11, 12]. The integrated architecture in this manner ensures the detection of undesired operations and enables generation of immediate responses. This, in turn, improves the quality, minimizes downtime and improves overall equipment efficiency. Through digital operations and simulation technology (empowering digital twins) allow to virtually experiment manufacturing operations and realize possible shortcomings which could be prevented before actual set up.

Smart operations also empower manufacturing systems in terms of optimizing the overall supply chain. Operations like demand forecasting, inventory optimization, supplier monitoring and performance assessments can be made more effective through intelligent methods and internet of things with equipped sensors.

Visual system manipulations, on the other hand, seem to be more and more demanding in manufacturing sites especially after the introduction of 5G. Being able to experiment manufacturing operations in virtual world and generate digital replicates of the manufacturing operations, make the life easy for designers to be able to discover and foresee the effect of new business models, new technology implementations and smart decision making capabilities.

It should be noted that the technological progress and disruptive technologies improve competitive advantage and makes it possible to devices systems which are superior over traditional automation systems. Intelligent manufacturing operations and the capability of real time data processing, on the other hand, allows prediction of possible failures before they actual occur and highlights possible anomalies. This may encourage predictive maintenance and prevent downtime and machine failures. With this respect, the quality management in integrated manufacturing environment is essential in order to sustain competitive manufacturing.

## **4. A general framework for intelligent quality**

Among various other applications, smart systems can be used to assure process and product quality. One of the distinguishing character of intelligent quality in recent years is that, not only the quality of the productions processes is the subject of quality management but also all processes within the production life cycle of the enterprise including the quality of procurement, design, planning, production, distribution and marketing. The applications can therefore be grouped into 3 categories as intelligent quality implementations before, during and after manufacturing operations. To mention some of those, smart manufacturing systems are to be capable of assuring quality through experimental design before production process starts [13]. During the production process some statistical process control (SPC) systems ca be well effectively employed thorough intelligent analysis and implementations [14]. Similarly defect detection after production is subject to intelligent operations. Similarly, visual inspection technology based on computer vision and machine learning is not strange in quality management societies. Wang et al. (2019) presented a computer vision based inspection system by employing deep learning in order to identify and classify defective products without the loss of accuracy [15]. Chesalin et al. (2020) provided an overview of intelligent quality tools that can be utilized for handling quality related issues within a manufacturing suit [16].

Similarly, reviewing respective literature highlights huge amount of areas where intelligent and smart systems are being utilized for the sake of generating a better quality on processes, products and services provided. Some of them can be listed below.

**5**

*Introduction to Intelligent Quality Management DOI: http://dx.doi.org/10.5772/intechopen.94971*

and detecting possible anomalies.

machines are healthy enough to operate.

terms of expected yield.

segmentation.

(experimental design)

counterparts.

activities.

activities.

manufacturers.

• Sustaining the quality of processes, products and services, ensuring reliability

• Predicting the behavior of the machines, devices and respective equipment in

• Performing computer vision based inspection through implementing machine

• Implementing intelligent systems to optimize quality parameters of the process

• Benchmarking which would allow the systems to be compared with their

Considering the complexity and functionality of products and process and the need for handling huge amount of data, sustaining required level of quality is nearly impossible without taking the advantages of information technologies. Enriching those with intelligent capabilities (utilizing artificial intelligence) and data analytics (big data manipulation) definitely improve the performance of quality management

Another aspect of intelligent quality in modern manufacturing systems is to sustain continuous improvement as was deployed by human quality operators and process teams. This definitely require communication of various manufacturing agents designed to exchange required knowledge which can trigger improvement

As above explanations clearly imply, well known artificial intelligence methodologies such as expert systems, machine learning (neural networks), genetic algorithms, fuzzy logic and some others are proven to be good enough for generating solutions to respective quality situations. However, for some situations these methodologies may not generate required responses alone. It may be inevitable to utilize more than one of these. Intelligent agents are capable of utilizing various AI methodologies depending upon the nature of the problem. The studies experimented indicates more effective and solution providers to the

**4.1 Requirements for developing intelligent quality management systems**

Intelligent quality requires management commitment in order to provide necessary facilities and capabilities for complying with the requirements and continually improve the effectiveness of the quality system. It should allow quality responsible

• Maintaining machine and condition monitoring for making sure that the

• Assuring the effectiveness of whole supply chain from suppliers to the customers including managing the respective resources as well as customer

• Handling inventory and optimizing material management.

learning techniques in order to find out defective products

• Generating alerts for process abnormalities and fault detection.

actual set up.

implies that manufacturing systems should employ knowledge protocols or any other knowledge transfer language [11, 12]. The integrated architecture in this manner ensures the detection of undesired operations and enables generation of immediate responses. This, in turn, improves the quality, minimizes downtime and improves overall equipment efficiency. Through digital operations and simulation technology (empowering digital twins) allow to virtually experiment manufacturing operations and realize possible shortcomings which could be prevented before

Smart operations also empower manufacturing systems in terms of optimizing the overall supply chain. Operations like demand forecasting, inventory optimization, supplier monitoring and performance assessments can be made more effective

Visual system manipulations, on the other hand, seem to be more and more demanding in manufacturing sites especially after the introduction of 5G. Being able to experiment manufacturing operations in virtual world and generate digital replicates of the manufacturing operations, make the life easy for designers to be able to discover and foresee the effect of new business models, new technology

It should be noted that the technological progress and disruptive technologies improve competitive advantage and makes it possible to devices systems which are superior over traditional automation systems. Intelligent manufacturing operations and the capability of real time data processing, on the other hand, allows prediction of possible failures before they actual occur and highlights possible anomalies. This may encourage predictive maintenance and prevent downtime and machine failures. With this respect, the quality management in integrated manufacturing

Among various other applications, smart systems can be used to assure process and product quality. One of the distinguishing character of intelligent quality in recent years is that, not only the quality of the productions processes is the subject of quality management but also all processes within the production life cycle of the enterprise including the quality of procurement, design, planning, production, distribution and marketing. The applications can therefore be grouped into 3 categories as intelligent quality implementations before, during and after manufacturing operations. To mention some of those, smart manufacturing systems are to be capable of assuring quality through experimental design before production process starts [13]. During the production process some statistical process control (SPC) systems ca be well effectively employed thorough intelligent analysis and implementations [14]. Similarly defect detection after production is subject to intelligent operations. Similarly, visual inspection technology based on computer vision and machine learning is not strange in quality management societies. Wang et al. (2019) presented a computer vision based inspection system by employing deep learning in order to identify and classify defective products without the loss of accuracy [15]. Chesalin et al. (2020) provided an overview of intelligent quality tools that can be utilized for handling quality related issues within a manufacturing suit [16]. Similarly, reviewing respective literature highlights huge amount of areas where intelligent and smart systems are being utilized for the sake of generating a better quality on processes, products and services provided. Some of them can be

through intelligent methods and internet of things with equipped sensors.

environment is essential in order to sustain competitive manufacturing.

implementations and smart decision making capabilities.

**4. A general framework for intelligent quality**

**4**

listed below.


Considering the complexity and functionality of products and process and the need for handling huge amount of data, sustaining required level of quality is nearly impossible without taking the advantages of information technologies. Enriching those with intelligent capabilities (utilizing artificial intelligence) and data analytics (big data manipulation) definitely improve the performance of quality management activities.

Another aspect of intelligent quality in modern manufacturing systems is to sustain continuous improvement as was deployed by human quality operators and process teams. This definitely require communication of various manufacturing agents designed to exchange required knowledge which can trigger improvement activities.

As above explanations clearly imply, well known artificial intelligence methodologies such as expert systems, machine learning (neural networks), genetic algorithms, fuzzy logic and some others are proven to be good enough for generating solutions to respective quality situations. However, for some situations these methodologies may not generate required responses alone. It may be inevitable to utilize more than one of these. Intelligent agents are capable of utilizing various AI methodologies depending upon the nature of the problem. The studies experimented indicates more effective and solution providers to the manufacturers.
