# A Primer on the Taguchi Method

Society of Manufacturing Engineers, 1990 - 247 sayfa
A clear, simple and essentially non-mathematical presentation, this practical guide introduces you to the basic concepts, techniques and applications of the renowned Taguchi approach. A Primer on the Taguchi Method introduces the fundamental concepts of Taguchi experimental design and shows engineers how to design, analyze, and interpret experiments using the Taguchi approach for a wide range of common products and processes. Written for manufacturing and production engineers, as well as design engineers and managers, this book explains the most practical ways to apply the Taguchi approach. The Taguchi approach to quality: the power of the Taguchi approach shows how it can be applied to an array of products from automobiles to computers. Learn the extraordinary benefits of building quality into the design, the heart of the Taguchi technique. Numerous real-world examples will help you see how the Taguchi Method works in a variety of manufacturing applications.For those who need a more rigorous statistical treatment, the book's working appendices provide full mathematical details on orthogonal arrays, triangular tables and linear graphs, plus fully worked solutions to problems presented in the example case studies.

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### İçindekiler

 Quality Through Product and Process Optimization 11 EXERCISES 11 The Taguchi Approach to Quality and Cost Improvement 11 22 TAGUCHI PHILOSOPHY 11 23 THE CONCEPT OF THE LOSS FUNCTION 11 24 EXPERIMENT DESIGN STRATEGY 14 25 ANALYSIS OF RESULTS 16 26 AREAS OF APPLICATION Analysis 17
 512 REPETITIONS UNDER CONTROLLED NOISE CONDITIONS 94 513 DESIGN AND ANALYSIS SUMMARY 96 EXERCISES 98 Analysis of Variance ANOVA 100 63 ONE WAY ANOVA 106 64 ONE FACTOR TWO LEVEL EXPERIMENTS ONE WAY ANOVA 112 641 Confidence Intervals 116 65 TWO WAY ANOVA 117

 EXERCISES 18 Measurement of Quality 19 32 VARIATION AS A QUALITY YARDSTICK 20 33 COST OF VARIATION 22 36 THE TAGUCHI QUALITY STRATEGY 23 37 SELECTING DESIGN PARAMETERS FOR REDUCED VARIATION 24 38 COMMON TERMINOLOGY 27 EXERCISES 28 Procedures of the Taguchi Method and Its Benefits 29 42 UPFRONT THINKING 31 44 EFFECTIVE USE OF STATISTICAL PROCESS CONTROL 32 46 QUANTIFYING COST BENEFITSTAGUCHI LOSS FUNCTION 33 47 SPECIFYING TOLERANCE LEVELS 37 EXERCISES 39 Working Mechanics of the Taguchi Design of Experiments 40 53 DESIGNING THE EXPERIMENT 44 531 Order of Running the Experiments 46 532 Analysis of the Results 47 533 Quality Characteristics 49 534 ANOVA Terms and Notations 50 536 Degrees of Freedom DOF 52 537 Projection of the Optimum Performance 55 54 DESIGNING WITH MORE THAN THREE VARIABLES 56 542 Designs with 3 Level Variables 58 551 Steps in the Design and Analysis Degrees of Freedom DOF 62 552 Interaction Effects 68 553 Key Observations 71 554 More Designs with Interactions 72 56 DESIGNS WITH MIXED FACTOR LEVELS 75 561 Preparation of a 4 Level Column 77 562 Preparation of an 8 Level Column 79 57 DUMMY TREATMENT COLUMN DEGRADING 82 58 COMBINATION DESIGN 87 59 DESIGNING EXPERIMENTS TO INVESTIGATE NOISE FACTORS 90 510 BENEFITING FROM REPETITIONS 91 511 DEFINITION OF SN RATIO 92
 66 EXPERIMENTS WITH REPLICATIONS 121 661 Procedures for Pooling 124 67 STANDARD ANALYSIS WITH SINGLE AND MULTIPLE RUNS 126 672 Pooling 134 673 Confidence Interval of Factor Effect 138 674 Estimated Result at Optimum Condition 139 675 Confidence Interval of the Result at the Optimum Condition 140 676 Analysis with Multiple Runs 142 68 APPLICATION OF THE SN RATIO 145 682 Advantage of SIN Ratio over Average 146 683 Computation of the SN Ratio 148 684 Effect of the SN Ratio on the Analysis 150 685 When to Use the SN Ratio for Analysis 154 EXERCISES 155 Loss Function 156 72 AVERAGE LOSS FUNCTION FOR PRODUCT POPULATION 160 EXERCISES 171 BrainstormingAn Integral Part of the Taguchi Philosophy 172 83 TOPICS OF THE DISCUSSIONS 174 84 TYPICAL DISCUSSIONS IN THE SESSION 175 EXERCISES 178 Examples of Taguchi Case Studies 180 92 APPLICATION EXAMPLES INCLUDING DESIGN AND ANALYSIS 181 Study of Crankshaft Surface Finishing Process 185 Automobile Generator Noise Study 189 Engine Idle Stability Study 192 Instrument Panel Structure Design Optimization 194 Study Leading to the Selection of the Worst Case Barrier Test Vehicle 197 Airbag Design Study 202 Transmission Control Cable Adjustment Parameters 203 References 209 Orthogonal arrays triangular tables and linear graphs 210 FTables 217 Glossary 229 Index 231 Telif Hakkı

### Popüler pasajlar

Sayfa 11 - ... money was expended in engineering experimentation and testing. Little emphasis was given to the process of creative brainstorming to minimize the expenditure of resources. Dr. Taguchi started to develop new methods to optimize the process of engineering experimentation. He developed techniques that are now known as the Taguchi Methods. His greatest contribution lies not in the mathematical formulation of the design of experiments, but rather in the accompanying philosophy. His approach is more...
Sayfa 11 - Reproduced by permission. approach is more than a method to lay out experiments. His is a concept that has produced a unique and powerful quality improvement discipline that differs from traditional practices. These concepts are: 1 . Quality should be designed into the product and not inspected into it. 2. Quality is best achieved by minimizing the deviation from a target. The product should be so designed that it is immune to uncontrollable environmental factors. 3 . The cost of quality should be...
Sayfa 11 - The technique of defining and investigating all possible conditions in an experiment involving multiple factors is known as the design of experiments.
Sayfa 11 - Taguchi divides quality control efforts into two categories: on-line quality control and off-line quality control. On-line quality control involves diagnosis and adjustment of the process, forecasting and correction of problems, inspection and disposition of product, and follow-up...
Sayfa 11 - While system design helps to identify the working levels of the design factors, parameter design seeks to determine the factor levels that produce the best performance of the product/process under study. The optimum condition is selected so that the influence of the uncontrolled factors (noise factors) causes minimum variation of system performance.

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