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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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This chapter contains exercises to enhance your understanding and application of Taguchi Methods. These exercises are applicable for company-wide training as well as for personal study. Exercises designated by an asterisk (*) are for advanced students; beginners may skip those exercises. This chapter also contains several two-step optimization and classical experimental design activities.

22.1 Methods to Assign Experimental Factors to Orthogonal Arrays
22.2 Sliding Level Method to Assign Correlated Factors to Orthogonal Arrays
22.3 Raw Data Transformation Through S/N (Signal-to-Noise) Ratio and Sensitivity
22.4 Data Analysis Based on Orthogonal Arrays
22.5 Optimization of an Automatic Screening Machine (Two Types of Errors in a Digital Input/Output System)
22.6 Operating Window Extension Method (From Chapter 5, Section 2 of New Experimental Design Methods)
22.7 Tolerance Design for Parts and Manufacturing Process
22.8 MTS (Mahalanobis-Taguchi System and Gradual Categorization Method)
22.9 Experimental Design for Product Assembly
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