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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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In this chapter, the author applies a real-life gold-plating case study to illustrate the robustness optimization procedure using an L18(2137) orthogonal array, measured raw data, and S/N (signal-to-noise) ratios of three static characteristics (nominal-the-best, larger-the-better, and smaller-the-better). Additionally, a two-step optimization procedure for a nominal-the-best type characteristic and a procedure for categorical output responses (such as appearance judgment based on visual inspection) are discussed.

5.1 Experimental Design Checklist of Robustness Optimization for Business Administrators and Management (Step 1)
5.2 Set-up Targets for the Design Objective (Step 2)
5.3 Generate as Many Factors as Possible, Classify the Factors, and Develop a Cause-and-Effect Diagram (Step 3)
5.4 Categorize the Factors (Step 4)
5.5 Selection of Orthogonal Arrays (Step 5)
5.6 Number of Factor Levels and Range of Levels (Step 6)
5.7 Experimental Factors and Levels in an L18(2137) Orthogonal Array (Step 7)
5.8 Selection of Noise Factors (Step 8)
5.9 Sequence of Experimental Runs (Step 9)
5.10 Conduct Comparative Experiments (Step 10)
5.11 Data Transformation (Static Type S/N Ratios) for Optimization of Experimental Output (Step 11)
5.12 Optimization Procedure for the Control Factors (Step 12)
5.13 Confirmation of the Estimate From the Main-Effect Plots (Step 13)
5.14 Selection of Optimal Design Candidates (Step 14)
5.15 Adjustment of the Gold-Plating Thickness to 5 Micron (Step 15)
5.16 Optimal Settings and Confirmation Experiment (Step 16)
5.17 Applying the Optimal Settings to the Production Process (Step 17)
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