Design of Experiments Using The Taguchi Approach

Over the past decade, Design of Experiments (DOE) has undergone great advances through the work of the Japanese management guru Genechi Taguchi. Yet, until now, books on the Taguchi method have been steeped in theory and complicated statistical analysis. Now this trailblazing work translates the Taguchi method into an easy-to-implement 16-step system.

Based on Ranjit Roy’s successful Taguchi training course, this extensively illustrated book/CD-ROM package gives readers the knowledge and skills necessary to understand and apply the Taguchi method to engineering projects–from theory and applications to hands-on analysis of the data. It is suitable for managers and technicians without a college-level engineering or statistical background, and its self-study pace–with exercises included in each chapter–helps readers start using Taguchi DOE tools on the job quickly. Special features include:

• An accompanying CD-ROM of Qualitek-4 software, which performs calculations and features all example experiments described in the book
• Problem-solving exercises relevant to actual engineering situations, with solutions included at the end of the text
• Coverage of two-, three-, and four-level factors, analysis of variance, robust designs, combination designs, and more

Engineers and technical personnel working in process and product design–as well as other professionals interested in the Taguchi method–will find this book/CD-ROM a tremendously important and useful asset for making the most of DOE in their work.

Contents

• What is Design of Experiments and Taguchi Approach.
• Definition and Measurement of Quality.
• Common Experiments and Methods of Analyses.
• Experiment Designs Using Orthogonal Arrays.
• Experiments With 2-level Factors Only.
• Experiments With 3-level and 4-level Factors.
• Analysis of Variance (ANOVA).
• Experiments to Study Interactions Between Factors.
• Experiments Design with Mixed Level Factors.
• Combination Designs.
• Strategies for Robust Design.
• Analysis Using Signal to Noise Ratios(S/N).
• Results Comprising of Multiple Criteria of Evaluations.
• Quantification of Variation Reduction and Performance Improvement.
• Effective Experiment Preparation and Planning.
• Example Case Studies.
• Appendix.
• List of Symbols.