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Taguchi methods

About: Taguchi methods is a research topic. Over the lifetime, 11626 publications have been published within this topic receiving 168455 citations. The topic is also known as: Quality Engineering.


Papers
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Journal ArticleDOI
TL;DR: In this paper, a new comprehensive approach to select cutting parameters for damage-free drilling in carbon fiber reinforced epoxy composite material is presented, based on a combination of Taguchi's techniques and on the analysis of variance (ANOVA).

431 citations

Book
01 Oct 1992
TL;DR: This book focuses on one of Taguchi's core techniques, "Design of Experiments", which helps engineers test their products and processes and design robust products at the lowest possible cost.
Abstract: American industry is now realizing that applying Dr. Genichi Taguchi's now-famous quality-engineering techniques can improve their products and produce substantial savings in cost and time. Until now, it has been difficult to find a clear explanation of the key terms and principles of Taguchi's methods. In Peace's book, industrial engineers will discover a practical, readable guide that demonstrates Taguchi techniques step-by-step. Unique coverage of the different types of quality characteristics ensure that readers will understand how to measure and choose options when applying this technology. The book focuses on one of Taguchi's core techniques, "Design of Experiments", which helps engineers test their products and processes and design robust products at the lowest possible cost. Case studies illustrate Taguchi methods at work in a varietyof situations.

421 citations

Journal ArticleDOI
TL;DR: The application and comparison of the Taguchi methodology has been emphasized with specific case studies in the field of biotechnology, particularly in diverse areas like fermentation, food processing, molecular biology, wastewater treatment and bioremediation.
Abstract: Success in experiments and/or technology mainly depends on a properly designed process or product. The traditional method of process optimization involves the study of one variable at a time, which requires a number of combinations of experiments that are time, cost and labor intensive. The Taguchi method of design of experiments is a simple statistical tool involving a system of tabulated designs (arrays) that allows a maximum number of main effects to be estimated in an unbiased (orthogonal) fashion with a minimum number of experimental runs. It has been applied to predict the significant contribution of the design variable(s) and the optimum combination of each variable by conducting experiments on a real-time basis. The modeling that is performed essentially relates signal-to-noise ratio to the control variables in a 'main effect only' approach. This approach enables both multiple response and dynamic problems to be studied by handling noise factors. Taguchi principles and concepts have made extensive contributions to industry by bringing focused awareness to robustness, noise and quality. This methodology has been widely applied in many industrial sectors; however, its application in biological sciences has been limited. In the present review, the application and comparison of the Taguchi methodology has been emphasized with specific case studies in the field of biotechnology, particularly in diverse areas like fermentation, food processing, molecular biology, wastewater treatment and bioremediation.

414 citations

Journal ArticleDOI
TL;DR: In this paper, the authors used the L9 orthogonal array in a CNC turning machine to optimize turning parameters based on the Taguchi method to minimize surface roughness (Ra and Rz).

408 citations

Book
01 Aug 1995
TL;DR: In this article, the Taguchi Method of Quality Engineering is used to evaluate the effect of noise factors on robustness and robustness of an industrial system, and a comparison of the two-stage optimization process is presented.
Abstract: Foreword. Preface. 1. Introduction to Quality Engineering. An Overview. The Concept of Noise in Robust Design. Product Reliability and Quality Engineering. What Is Robustness? What Is Quality? On-Target Engineering. How Is Quality Measured? The Phases of Quality Engineering in Product Commercialization. Off-Line Quality Engineering. On-Line Quality Engineering. The Link between Sir Ronald Fisher and Dr. Genichi Taguchi. A Brief History - The Taguchi Method of Quality Engineering. Concluding Remarks. Exercises for Chapter 1. I. QUALITY ENGINEERING METRICS. 2. Introductory Data Analysis for Robust Design. The Nature of Data. Graphical Methods of Data Analysis. Quantitative Methods of Data Analysis. An Introduction to the Two-Step Optimization Process. Summary. Exercises for Chapter 2. 3. The Quality Loss Function. The Nature of Quality. Relating Performance Distributions to Quality. The Step Function: An Inadequate Description of Quality. The Customer Tolerance. The Quality Loss Function: A Better Description of Quality. The Quality Loss Coefficient. An Example of the Quality Loss Function. The Types of Quality Loss Functions. Loss Function Case Study. Summary. Exercises for Chapter 3. 4. The Signal-to-Noise Ratio. Properties of the S/N Ratio. Derivation of the S/N Ratio. Defining the Signal-to-Noise Ratio from the Mean Square Derivation. Identifying the Scaling Factor. Summary. Exercises for Chapter 4. 5. The Static Signal-to-Noise Ratios. Introduction, Static vs. Dynamic Analysis. The Smaller-the-Better Type Signal-to-Noise Ratio. The Larger-the-Better S/N Ratio. The Operating Window: A Combination of STB and LTB. A Signal-to-Noise Ratio for Probability. The Nominal-the-Best Signal-to-Noise Ratios. Two-Step Optimization. A Comparative Analysis of Type I NTB and Type II NTB. A Note on Notation. Summary. Exercises for Chapter 5. 6. The Dynamic Signal-to-Noise Methods and Metrics. Introduction. The Zero-Point Proportional Case. The Reference-Point Proportional Case. Nonlinear Dynamic Problems. The Double-Dynamic Signal-to-Noise Ratio. Summary. Exercises for Chapter 6. II. PARAMETER DESIGN. 7. Introduction to Designed Experiments. Experimental Approaches. The Analysis of Means (ANOM). Degrees of Freedom. Full Factorial Arrays. Fractional Factorial Orthogonal Arrays. Summary of Chapter 7. Exercises for Chapter 7. 8. Selection of the Quality Characteristics. Introduction. Engineering Analysis in the Planning Stage. The Ideal Function of the Design. Guidelines for Choosing the Quality Characteristic. Summary: The P-diagram. Exercises for Chapter 8. 9. The Selection and Testing of Noise Factors. Introduction. The Role of Noise Factor - Control Factor Interactions. Experimental Error and Induced Noise. Noise Factors. Choosing the Noise Factors. The Noise Factor Experiment. Analysis of Means for Noise Experiments. Examples. Other Approaches to Studying Noise Factors. Case Study: Noise Experiment on a Film Feeding Device. Summary of Chapter 9. Exercises for Chapter 9. 10. The Selection of Control Factors. Introduction. Selecting Control Factors to Improve Tunability and Robustness. Selecting and Grouping Engineering Parameters to Promote Additivity. Sliding Levels for Control Factors. Example: The Catapult. Example: The Paper Gyrocopter. Summary: The P-diagram. Exercises for Chapter 10. 11. The Parameter Optimization Experiment. Introduction. Dr. Taguchi's Parameter Design Approach. Layout of the Static Experiment. Layout of the Dynamic Experiment. Choosing the Noise Factor Treatment. Choosing the S/N Ratio. Summary of Chapter 11. Exercises for Chapter 11. 12. The Analysis and Verification of the Parameter Optimization Experiment. Introduction. The Data Analysis Procedure. An Example of the Analysis of the Parameter Optimization Experiment. Estimating the Effects of Each Factor Using ANOM. Identifying the Optimum Control Factor Set Points. The Two-Step Optimization Process. The Additive Model. The Predictive Equation. The Verification Tests. Summary: Succeeding at Parameter Design. Exercises for Chapter 12. 13. Examples of Parameter Design. The Ice Water Experiment: Smaller-the-Better. The Gyrocopter Experiment: Dynamic Larger-the-Better. The Catapult Experiment. Conclusion. Exercises for Chapter 13. 14. Parameter Design Case Studies. Introduction. Paper Handling - An Operating Window Example with Two Signal Factors. Improvement of a Capstan Roller Printer Registration. Enhancement of a Camera Zoom Shutter Design. Summary. III. ADVANCED TOPICS. 15. Modifying Orthogonal Arrays. Introduction. Downgrading a Column. Upgrading a Column. Compound Factors. Summary of Chapter 15. Exercises for Chapter 15. 16. Working with Interactions. The Nature of Interactions in Robust Design. Interactions Defined. How Interactions Are Measured. Degrees of Freedom for Interactions. Setting Up the Experiment When Interactions Are Included. Summary of Chapter 16. Exercises for Chapter 16. 17. Analysis of Variance (ANOVA). Introduction. An Example of the ANOVA Process. Degrees of Freedom. Error Variance and Pooling. Error Variance and Replication. Error Variance and Utilizing Empty Columns. The F-Test. WinRobust Examples. Summary. Exercises for Chapter 17. 18. The Relationship of Robust Design to Other Quality Processes. Quality Function Deployment (QFD) and Robust Design. Design of Experiments and Robust Design. Six Sigma Quality Process and Robust Design. Summary. Appendix A Glossary. Appendix B Quick Start Guide for WinRobust Lite. Appendix C Orthogonal Arrays. Appendix D Bibliography. Index. 0201633671T04062001

384 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023932
20222,147
20211,113
2020969
2019885
2018930