## A Primer on the Taguchi MethodA 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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### Contents

1 | |

5 | |

7 | |

8 | |

10 | |

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 |

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### Common terms and phrases

analysis of variance assigned to columns average of performance brainstorming session cake calculated COLUMN FACTORS LEVEL combination computed confidence interval confidence level Current grand average degrees of freedom DESCRIPTION LEVEL DESCRIPTION design of experiments determine engineering error DOF error term error variance estimate Example experiment design FACTOR DESCRIPTION LEVEL factor effects factorial experiment Figure influence LEVEL 3 LEVEL LEVEL DESCRIPTION LEVEL level factors linear graph loss function measured molding process noise factors number of experiments Number of levels number of repetitions Observation Observation Observation Optimization optimum condition orthogonal array outer array pooled pure sum quality characteristic result at optimum S/N ratio selected shown in Table significant smaller is better SQUARES VARIANCE standard sum of squares Taguchi approach Taguchi experiment Taguchi method target value tolerance total number trial conditions trial runs triangular table variables Variance ratio variation z z z

### Popular passages

Page 7 - ... 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...

Page 8 - 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...

Page 1 - The technique of defining and investigating all possible conditions in an experiment involving multiple factors is known as the design of experiments.

Page 10 - 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...

Page 10 - 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.