**2. Literature review**

Taguchi observed that the most important reason for a product to be rejected is variability in product specifications. Improving quality is through reducing variability. Efforts for quality should be made for zero deviation and zero distortion. All quality experts, especially Shewart and Deming, have addressed the issue of variability. Taguchi in one of his articles [3] -by using the **Figure 1** which has given under the title "Who is the Better Marksman?"- indicated that it is a difficult problem to eliminate variability in the production process. In this example, both gunners fire ten shots. If the average position of Gunner's A is calculated, it will be seen that the average is very close to the target. On the other hand, marksman B's average is far from the target. However, his shots are very consistent. When the variability is calculated for both marksmen, it will be seen that the variability of the gunner B is much less. Those who are interested in shooting can easily say that while it is possible to correct B's shots with a small adjustment, it will take a lot of effort to make A a good shooter. Taguchi argues that production processes are also similar to shooters in this respect. While it is possible to easily adjust the B sniper-like processes, improving the A sniper-like processes will take a lot of time, maybe even

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

Taguchi proposes a two-step process to reduce product variability. These steps

• To produce the product with the best methods, technology and techniques

In order to fulfill above issues, Taguchi divides the activities into two parts as On-Line Quality Control and Off-Line Quality Control. While on-line quality control covers the quality activities during and after the manufacture of the product, off-line quality control includes market research and quality activities carried out during the development of the product and production process. These activities are design studies carried out before production begins. Taguchi defines three stages such as system design, parameter design, and tolerance design both for product and

The most important stage of product or process design in terms of quality improvement is the parameter design stage. At this stage, DOE method is used to

• To produce all products in the same way

huge investments.

process improvement.

**Figure 1.**

**112**

*Who is better gunner? (Adapted from Ref. [3]).*

are as follows.

RD is an important technique for product manufacturability and product life. Although the method was known by 1960's in Japan it has been used in USA by 1980's. Since its use in the USA industry in the 1980s, it has attracted a great attention from designers, manufacturers, statisticians and quality experts. Due to this success of robust design, a lot of researches such as master and PhD theses, scientific articles and case studies have been done to understand the method. Literature of Taguchi Method (TM) and RD is very large and it is still growing. When the literature is examined, it will be seen that Tagcuhi method is frequently used for the optimization of critical parameters of product and process in manufacturing industry and it gives useful results. **Table 1** presents some examples from last ten years publications about the manufacturing industry. It is important to note that TM has been applied to the service industry too. Antony [5] reports the potential applications of DOE in the service environment as follows.


Recently publishings deal with the integration of TM and other approaches such as multicriteria decision making (MCDM), principal component analysis, numerical simulation, artificial neural network, and genetic algorithm. Sharma et al. [6] used the TM and PROMETHEE (which widely used MCDM tool) technique to obtain an optimal setting of process parameters for single and multi-optimization resulting in an optimal value of the material removal rate and tool wear rate. Kumar and Mondal [7] compared the results of experimental data on the electric discharge machining of AISI M2 steel by different optimization techniques such as TM, TOPSIS and gray relational analysis (GRA). Viswanathan et al. [8] aimed to investigate the effective factors in turning of magnesium alloy with physical vapor deposition coated carbide insert in dry conditions. To identify the optimal parameters setting, a combination of principal component analysis (PCA) and GRA has been conducted. Liu et al. [9] and, Land and Yeh [10] used both TM and ANSYS which widely used numerical simulation software in order to optimize and design injection molded products. Asafa et al. [11] presented integration of TM and artificial neural network (ANN) technique for the prediction of intrinsic stresses induced during plasma enhanced chemical vapor deposition of hydrogenated amorphous silicon thin films. Parinam


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

• External noise factors: factors such as environmental conditions, eg; environmental temperature, workers, different raw material piles etc.

• Intrinsic noise factors: time-varying factors, eg; deterioration, aging,

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

• How to reduce variability when the product is in customer use? How does a product consistently perform at the desired property and thus maximize

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

RD covers the parameter design and tolerance design steps of TM. System design

In order to realize RD, it is necessary to follow a systematic path. Implementa-

2.Determination of performance characteristics and measurement system

Uncontrollable factors can be divided into three categories.

• Product-related factors: the difference in each product

conditions.

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

discoloration, etc.

questions [29].

customer satisfaction?

• How is the production process optimized?

Taguchi's off-line quality control system is DOE [30].

tion of the below steps are beneficial [26, 32, 33].

4.Establishing the monitoring design

**115**

consists of traditional research and development activities [31].

1.Determining the problem and organizing the experiment team

3.Determining the variables affecting performance characteristics

#### **Table 1.**

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

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