Table Of ContentTaguchi Methods
Benefits, Impacts, Mathematics,
Statistics, and Applications
by Teruo Mori, PhD, PE
translated by Shih-Chung Tsai, PhD, CQE
© 2011, ASME, 3 Park Avenue, New York, NY 10016, USA (www.asme.org)
Library of Congress Cataloging-in-Publication Data
Mori, teruo, 1946-
taguchi methods : benefits, impacts, mathematics, statistics, and applications/
by teruo Mori ; translated by Shih-Chung tsai.
p. cm.
1. taguchi methods (quality control) 2. quality control–Mathematics. 3. logic. I. title.
tS156.M64513 2011
658.5’62–dc22
2011015818
Contents
Foreword xix
Recommendations xxiii
Preface xxix
1
Taguchi Methods for Challenges
in Manufacturing 1
1.1 Competition Among Manufacturing
Industries and business Administration
Strategy 3
1.2 Example: Solving a downstream quality
Problem Using a traditional Approach 12
1.3 robust design for downstream Noise
Conditions 14
1.4 reduction of output response variation 14
1.5 from a traditional Problem-Solving
Approach to a robust design Approach 21
1.6 Assessment of Energy transformation
Efficiency 23
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1.7 traditional and taguchi-Class
optimization Methods 34
1.8 Case Studies based on robust
design Procedures 41
2
Mathematical Structural of Orthogonal Arrays 47
2.1 traditional Methods to Select optimal
design Conditions 49
2.2 New optimization Method based on
orthogonal Arrays 50
2.3 Confirmation of the optimal Condition 53
2.4 Introduction to orthogonal Arrays 56
3
Methods to Select and Compound Noise Factors 61
3.1 fire-Extinguishing Problem:
Nagano olympics torch 63
3.2 output variation root Causes 64
3.3 Noise factor Selection 67
3.4 Noise factors and orthogonal Arrays
(outer Arrays) 68
3.5 Assigning a Signal factor and Noise
factors to an outer (orthogonal) Array 69
3.6 Compounding Noise factors for Preliminary
Experiments 70
3.7 Noise factors for Accelerated tests
(and overload tests) 71
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3.8 Noise factors for reliability tests 71
3.9 Surrogate Noise factors 73
3.10 Noise factors for Computer
Simulations 76
3.11 dr. taguchi’s quality Engineering
Strategies for Noise factors 78
4
Electric Motor Optimization Using Dynamic
S/N (Signal-to-Noise) Ratios 81
4.1 Electric Motor basic function and Its
Evaluation Characteristics 83
4.2 E lectric Motor optimization Using an
l orthogonal Array 87
18
5
S/N (Signal-to-Noise) Ratios for Static
Characteristics and the Robustness
Optimization Procedure 127
5.1 Experimental design Checklist of
robustness optimization for business
Administrators and Management (Step 1) 130
5.2 Set-up targets for the design objective
(Step 2) 130
5.3 generate as Many factors as Possible,
Classify the factors, and develop a
Cause-and-Effect diagram (Step 3) 131
5.4 Categorize the factors (Step 4) 133
5.5 Selection of orthogonal Arrays (Step 5) 135
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5.6 Number of factor levels and range
of levels (Step 6) 137
5.7 Experimental factors and levels in
an l (2137) orthogonal Array (Step 7) 141
18
5.8 Selection of Noise factors (Step 8) 143
5.9 Sequence of Experimental runs (Step 9) 145
5.10 Conduct Comparative Experiments (Step 10) 146
5.11 data transformation (Static type
S/N ratios) for optimization of
Experimental output (Step 11) 146
5.12 optimization Procedure for the
Control factors (Step 12) 153
5.13 Confirmation of the Estimate from
the Main-Effect Plots (Step 13) 158
5.14 Selection of optimal design Candidates
(Step 14) 161
5.15 Adjustment of the gold-Plating thickness
to 5 Micron (Step 15) 165
5.16 optimal Settings and Confirmation
Experiment (Step 16) 166
5.17 Applying the optimal Settings to the
Production Process (Step 17) 166
6
Standard Usage and Transformation of
Taguchi-Class Orthogonal Arrays 169
6.1 Experimental optimization Methods 171
6.2 factor Effects and output responses 172
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6.3 orthogonal Arrays for Product/Process
development 173
6.4 Assessment of Interaction Effects 174
6.5 orthogonal Arrays and the Number of
factor levels 178
6.6 Useful techniques to Assign Experimental
factors to orthogonal Arrays 186
6.7 A Case Study based on the Assignment
techniques from the Previous Sections Using
an l Array (Improvement of resin film
18
tensile Strength Case Study Using five-level
factors, dummy treatment, Infeasible runs,
and Missing data) 190
7
Taguchi Methods (Robust Design) and
Traditional Experimental Optimization
Procedures 205
7.1 traditional Experimental optimization
Procedures 209
7.2 Input-output relationship based on
Input-output Energy transformation
Approach 217
7.3 Improving the Effects of Individual factors 228
7.4 reproducibility of traditional Experimental
optimization Methods 232
7.5 traditional Experimental optimization
Methods versus taguchi Methods from
the viewpoint of business Administrators 233
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7.6 taguchi Methods in the United States 236
7.7 Summary: Comparisons between
traditional Experimental optimization
Methods and taguchi Methods 238
8
Historical Events and Milestone Case Studies
of Taguchi Methods 241
8.1 biography of dr. genichi taguchi
(from birth to Present) 243
8.2 Milestone taguchi Methods Case Studies 247
8.3 brief discussion of Commonly referenced
taguchi Methods Case Studies 250
9
Taguchi-Class Experimental Design Methods
and Applications 303
9.1 Case Studies based on the row-Assembly
Method of taguchi-Class Experimental
design (Using Idle Column and Columns
of three- and four-level factors) 306
9.2 Case Study based on Modified
taguchi-Class Experimental layouts 317
9.3 Categorization-and-grouping Analysis
and the Associated long-term reliability
Analysis 322
9.4 root Cause Identification for Product
defects based on an orthogonal layout 333
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9.5 Multiple Sets of Experiments based on
the Same orthogonal Array 335
9.6 treatment for Multiple randomly
Associated orthogonal Arrays 337
10
Energy Transformation/Tranfer and
Generic Function 351
10.1 transformation from first-level
Energy Into Second-level Energy 353
10.2 types of Energy transformation
Mechanisms 354
10.3 Energy transformation Mechanisms
Case Studies 358
10.4 brake System Improvement Case Study 373
10.5 Energy transferring Mechanism 375
10.6 S/N (Signal-to-Noise) ratios for Energy
transformation/transferring Mechanisms 380
10.7 q&A 382
11
Two-Step Design Optimization and
Tolerance Design
385
11.1 two-Step design optimization 387
11.2 tolerance design Applications 401
11.3 Summary 409
11.4 tolerance Specification differences 410
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12
Logarithm Transformation of the Output
Response Data for Optimization 413
12.1 functional Input-output relationship
and Control factor levels 415
12.2 Ideal function of Input-output
Energy transformation 416
12.3 A Metric of functional robustness for
design optimization 420
12.4 functional relationship between Input
Energy and output response 425
12.5 functional relationship of a System 428
12.6 Additivity of business Activities 430
13
Output Characteristics, Statistics, and
Calculation Examples of Taguchi Methods
433
13.1 dynamic and Static Characteristics 435
13.2 Classification and Assessment of Static
Characteristics 452
13.3 Analysis of Percentage data 468
13.4 Analysis of ranking or Categorical data 469
13.5 operating window Method 471
13.6 dynamic operating window Method
(Chemical reaction Example) 472
13.7 Analysis of digital data (optimization
based on two types of Error) 479
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