**3. Robust design**

Phadke defines the RD as an engineering methodology for improving productivity during research and development. Hence high-quality products can be produced quickly and at low cost [25]. The emphasis of RD is variability in product and process performance. Reducing variability will result in increased quality. The source of variability can be divided into two groups [26].

• Controllable factors: Factors determined by the manufacturer that cannot be changed directly by the customer,

• Uncontrollable factors (Noise factors): Factors that the producer cannot directly control and that vary according to customer use and environmental conditions.

Uncontrollable factors can be divided into three categories.


Hence, RD means a design that has minimum sensitivity to variabilty of uncontrollable factors. Taguchi says that it is necessary to minimize the variability in the product or process by choosing the values of the controllable factors (parameters) optimally against the factors that create variability. The word robust in the statement of RD refers to uncontrollable factors which insensitive to environmental conditions such as moisture, dust, heat, different applications in customer use and differences in materials [27, 28]. The key to Taguchi Robust Design; instead of trying to control factors that cannot be controlled or that are too expensive to control, it is to determine the best values of controllable factors that will minimize their effects on the product or process [27]. RD provides answers to the following questions [29].


As will be known, there are many factors that need to be determined and optimally adjusted in product and process parameter design stages. Moreover, many of these factors interact with each other. The most effective method to determine the effects of these controllable and uncontrollable factors on product and product performance is statistical experiment design. Through experimental design, it is possible to economically determine the effect of many factors on the product and to take precautions against factors that cause variability at the design stage. Therefore, we can say that the most important quality assurance method in Taguchi's off-line quality control system is DOE [30].

RD covers the parameter design and tolerance design steps of TM. System design consists of traditional research and development activities [31].

In order to realize RD, it is necessary to follow a systematic path. Implementation of the below steps are beneficial [26, 32, 33].


et al. [12] described integration of TM and Genetic Algorithms to optimize high

Taguchi optimization methodology is applied to optimize cutting parameters in high-

molding was reviewed. Also, integration of TM with various approaches including numerical simulation, GRA, PCA, ANN, and genetic algorithm (GA) were discussed.

The mix design of geopolymer concrete based on the target strength criteria by optimizing the proportions of the constituents using TM is presented. Terzioğlu [16] The factors which were effective in Thermoelectric Generators (TEG) used in the

production of electrical energy a research is carried out by using TM to determine the

internal thermal resistance are investigated. TM is carried out to obtain the optimal

The objective of the article is to optimize and design nano-biosystem of Isradipine via novel bioenhancer (Rutin) loaded solid-lipid nanobioparticles using Taguchi design

Taguchi design method for obtaining lower surface roughness values in terms of process parameters in wood machining is presented. Orthogonal arrays of Taguchi and the signal-to-noise (S/N) ratio is employed to find the optimal levels and to

press pressure, and pressing time on the thermal conductivity of oriented strand

Taguchi's DOE is used to investigate the main effects of four processing parameters in the Fused Deposition Modeling (FDM) process; those are the infill percentage,

ANOVA to reduce the variability in the Ride comfort of a vehicle with respect to

The optimization of the cutting parameters on drill bit temperature in drilling was evaluated by TM. TM was used to determining the settings of cutting parameters.

pressure jet assisted turning when machining Inconel 718. Fei et al. [14] The practical use of TM in the optimization of processing parameters for injection

Zhou et al. [17] The effects of eight parameters on the value of borehole thermal resistance and

Hong [18] A clustering approach based on TM for effective market segmentation is proposed. To select appropriate initial seeds, the use of TM as a tool is suggested.

analyze the effect of process parameters on surface roughness. Hamzaçebi [21] TM was applied to determine the effects of production factors such as adhesive ratio,

infill pattern, layer thickness, and extrusion temperature. Mitra et al. [23] TM of robust optimization has been adapted along with DOE methodology and

Phadke defines the RD as an engineering methodology for improving productivity during research and development. Hence high-quality products can be produced quickly and at low cost [25]. The emphasis of RD is variability in product and process performance. Reducing variability will result in increased quality. The

• Controllable factors: Factors determined by the manufacturer that cannot be

source of variability can be divided into two groups [26].

changed directly by the customer,

transmission optical filter.

**Article Subject**

performance effects.

methodology.

board.

*Some articles from the literature of the last ten years.*

sprung mass of vehicle.

scenarios of parameters combination.

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

Sekulic et al. [13]

Dave and Bhogayata [15]

Kumar et al. [19]

Tiryaki et al. [20]

Alafaghani and Qattawi [22]

Çakıroğlu and Acır [24]

**Table 1.**

**114**

**3. Robust design**


**3.5 Determining the number and levels of CV and UCV**

UCV in the experiment [34].

**3.6 Identifying possible interactions**

*Taguchi Method as a Robust Design Tool DOI: http://dx.doi.org/10.5772/intechopen.94908*

adjustment accordingly,

**Figure 2.**

**117**

*(c) Strong interaction.*

should be applied to reduce the interaction effects.

1.Determining the performance characteristics by weight,

between two factors can be determined by graphical procedure.

2.Determining the relationship between CV and its levels and making an

3.Conducting an analysis for classified data, such as cumulative analysis.

*Graphical representation of interaction between two factors. (a) No interaction, (b) Weak interaction,*

However, the experimenter must have the necessary attention and knowledge. It is difficult to add all interaction factors to the experiment due to the high cost and time required. On the other hand, including interaction factors believed to be important in the experiment will increase success. The existence of interaction

The number of levels of variables is determined by their characteristics. Thus, possible alternatives are obtained. Taguchi recommends selecting three or more test groups for each CV. Three or more test levels allow a nonlinear effect of CV on the performance characteristic to be revealed. Test levels should be chosen over a wide range so that the CV sequence covers a large region of the CV space. The next step is to determine the set of UCV. This cluster includes the values of the UCV that affect the performance variability the most or the product performance is insensitive. Due to physical impossibilities or lack of information, not all UCV can be included in the experiment. Therefore, it is important to represent all possible combinations of

The definition of interaction can be as follows: If the effect of a factor on the response variable depends on the value of the other factor, it is said that there is an interaction between two factors as seen in **Figure 2** [30]. The interactions can have a significant impact on performance characteristics. Taguchi thinks that interaction is not that important. The reason of this; the view is that in order to detect the interaction, the experimenter has to control the two main effects, and the interaction does not contribute anything when one or more of the main factors are under control [33]. Taguchi and Wu [35] suggest that one of the following techniques

