**3.8 Determination performance statistics**

Defining the optimal CV requires the determination of some criteria to be optimized such as Signal / Noise (*S=<sup>N</sup>*Þ ratio. The analysis of the data obtained from the experiment is made according to performance statistics and / or mean. Wrong selection of performance characteristics leads to erroneous determination of UCV levels and results. The *S=<sup>N</sup>* ratio is used to measure the best RD performance. Many different *S=<sup>N</sup>* ratios can be used depending on the purpose of the optimization process. Taguchi mentions that over than 60 *S=<sup>N</sup>* ratios can be used and that he developed most of them himself [2]. However, all *S=<sup>N</sup>* ratios must meet the criteria listed below [28].

and / or mean, Analysis of Variance (ANOVA) is made and percentage contribu-

2. Setting variables that have a significant effect on average but have no effect on

3.Residual variables that do not affect the average or performance statistics at all.

Analysis results are plotted according to the levels of CV, so that the effects are displayed visually. The optimization procedure is different. If the performance statistics are Nominal - Best, TM uses the following two-step procedure.

1. Investigation of CVs and levels for which the analyst expects the least

2. Investigation of the setting variables that will bring the sample mean to the

With this method, the variability is reduced in the first step and the sensitivity increases in the second step. If the performance statistics are the smallest best, the TM uses a one-step procedure. This procedure aims to reduce the total variance using the calculated performance statistics; CV affecting the total variance is investigated. Levels of CV where the analyst expects the smallest mean square variability are determined. If the performance statistics are the greatest best, the TM uses a twosided transformation. Performance statistics change from smallest to best, using the one-step method to reduce the total variance. In case of disagreement between different performance characteristics, one may be abandoned and then the best values selected. If the chosen CV combination is not included in the experiment, the performance values and confidence intervals of the best combination are estimated.

As we mentioned earlier, DOE is used to develop or improve products or processes. The data obtained from the experiment should be analyzed. Variance analysis is used to interpret experimental data. Variance analysis was used for the first time by the British statistician Fisher. Experts usually work with samples. Because it is sometimes impossible to work with the whole population and sometimes it is very expensive. It should not be forgotten that; each individual case study forms part of the error. Sample statistics and assumptions allow the testing of hypotheses regarding experimental parameters. In

Total variance can be divided into two components such as inter-group variabil-

ity and intra-group variability. The components of the model are tried to be

order to analyze variance with sample data, we have four basic assumptions.

variability, using calculated performance statistics.

target using the calculated sample mean or sample total.

tions are determined. Thus CV can be divided into three classes.

performance statistics,

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

*3.10.1 Analysis of variance (ANOVA)*

1.Samples are random,

**121**

2.Population is distributed normally,

4.The choice of samples is independent of the others.

3.Population variances are equal,

1.CV, which has a significant impact on performance statistics,


Many S / N ratios are available. The three commonly used are as below.


## **3.9 Establishing the experiment and recording the results**

The design optimization experiment can be done in two ways.


In both experiments, any combination of CV is tested for all combinations of UCV and the results are recorded. The order in which the experiments are performed should be random, as the process will not be constantly stationary. In order for the test results to be evaluated completely and precisely, the test conditions must be recorded.

#### **3.10 Analysis of data and selection of the best values of CV**

One of the goals of design optimization experiments is to reduce variability. Another goal is to adjust the mean to the target value. To achieve these two objectives, mean and performance statistics are calculated for each combination of CV in the design model. In order to evaluate the effects of CV on performance statistics

**3.8 Determination performance statistics**

*=*

process. Taguchi mentions that over than 60 *S*

developed most of them himself [2]. However, all *S*

optimized such as Signal / Noise (*S*

levels and results. The *S*

*=*

listed below [28].

variable.

changes.

purposes.

• Largest - Best

• Smallest - Best

• Nominal - Best

• Computer simulation.

conditions must be recorded.

**120**

1.The *S=*

2.The *<sup>S</sup>=*

3. *<sup>S</sup>=*

4. *<sup>S</sup>=*

different *S*

Defining the optimal CV requires the determination of some criteria to be

the experiment is made according to performance statistics and / or mean. Wrong selection of performance characteristics leads to erroneous determination of UCV

*<sup>N</sup>* ratios can be used depending on the purpose of the optimization

*<sup>N</sup>* ratio should reflect the variability of the UCV on the response

system should be useful in predicting the quality even if the target value

*<sup>N</sup>* ratio measures relative quality. Because it is used for comparative

Many S / N ratios are available. The three commonly used are as below.

*<sup>N</sup>* ratio should not cause unnecessary complexity.

**3.9 Establishing the experiment and recording the results**

**3.10 Analysis of data and selection of the best values of CV**

• Physical performance of the experiment,

The design optimization experiment can be done in two ways.

In both experiments, any combination of CV is tested for all combinations of

One of the goals of design optimization experiments is to reduce variability. Another goal is to adjust the mean to the target value. To achieve these two objectives, mean and performance statistics are calculated for each combination of CV in the design model. In order to evaluate the effects of CV on performance statistics

UCV and the results are recorded. The order in which the experiments are performed should be random, as the process will not be constantly stationary. In order for the test results to be evaluated completely and precisely, the test

*=*

*<sup>N</sup>* ratio is independent of setting the mean. This means; the measuring

*<sup>N</sup>*Þ ratio. The analysis of the data obtained from

*<sup>N</sup>* ratios can be used and that he

*<sup>N</sup>* ratios must meet the criteria

*<sup>N</sup>* ratio is used to measure the best RD performance. Many

*=*

*=*

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

and / or mean, Analysis of Variance (ANOVA) is made and percentage contributions are determined. Thus CV can be divided into three classes.


Analysis results are plotted according to the levels of CV, so that the effects are displayed visually. The optimization procedure is different. If the performance statistics are Nominal - Best, TM uses the following two-step procedure.


With this method, the variability is reduced in the first step and the sensitivity increases in the second step. If the performance statistics are the smallest best, the TM uses a one-step procedure. This procedure aims to reduce the total variance using the calculated performance statistics; CV affecting the total variance is investigated. Levels of CV where the analyst expects the smallest mean square variability are determined. If the performance statistics are the greatest best, the TM uses a twosided transformation. Performance statistics change from smallest to best, using the one-step method to reduce the total variance. In case of disagreement between different performance characteristics, one may be abandoned and then the best values selected. If the chosen CV combination is not included in the experiment, the performance values and confidence intervals of the best combination are estimated.
