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Taguchi Methods: Benefits, Impacts, Mathematics, Statistics and Applications
Teruo Mori, PhD
Teruo Mori, PhD
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Shih-Chung Tsai, PhD
Shih-Chung Tsai, PhD
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ASME Press
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Engineers apply experimental optimization methods to find input factor settings that improve system output responses. There are four major experimental optimization methods used in engineering applications.

1. Trial-and-error experimentation: This problem-solving approach uses experience and the judgment of engineers as well as production process operators. Typically, this approach solves emergent problems in production processes. Very limited experiments (two or three) are conducted when solving these problems. When the output responses meet or exceed the targets, the trial-and-error experiments stop immediately due to timing and cost concerns.

2. One-factor-at-a-time experimentation: In this approach, engineers conduct experiments on several potentially significant factors. However, only one factor is varied in each experiment; the other factors are fixed at default levels. In addition, the experimental environment and conditions are tightly controlled to determine the effects of these factors. This type of experimentation is used in academic research or in scientific studies conducted at universities.

3. Full factorial experimentation: In this approach, engineers conduct experiments on all possible combinations of all factor levels. A factorial experimental design is done for a small number of factors, but it is not practical for a large number of factors.

4. Traditional fractional factorial experimentation: In order to reduce the total number of experiments, engineers conduct experiments using fractional factorial designs in industrial experimentation. Orthogonal arrays like the L8, L16, L9, or L27 belong to this type of experimental design. In these designs, specific subsets of all possible factor level combinations are evaluated in the experiment.

6.1 Experimental Optimization Methods
6.2 Factor Effects and Output Responses
6.3 Orthogonal Arrays for Product/Process Development
6.4 Assessment of Interaction Effects
6.5 Orthogonal Arrays and the Number of Factor Levels
6.6 Useful Techniques to Assign Experimental Factors to Orthogonal Arrays
6.7 A Case Study Based on the Assignment Techniques From the Previous Sections Using an L18 Array (Improvement of Resin Film Tensile Strength Case Study Using Five-Level Factors, Dummy Treatment, Infeasible Runs, and Missing Data)
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