It is more than a decade since Genichi Taguchi's ideas on quality improvement were inrroduced in the United States. His parameter-design approach for reducing variation in products and processes has generated a great deal of interest among both quality practitioners and statisticians. The statistical techniques used by Taguchi to implement parameter design have been the subject of much debate, however, and there has been considerable research aimed at integrating the parameter-design principles with well-established statistical techniques. On the other hand, Taguchi and his colleagues feel that these research efforts by statisticians are misguided and reflect a lack of understanding of the engineering principles underlying Taguchi's methodology. This panel discussion provides a forum for a technical discussion of these diverse views. A group of practitioners and researchers discuss the role of parameter design and Taguchi's methodology for implementing it. The topics covered include the importance of vari...

Taguchi's parameter design: a panel discussion

Robust Design is an important method for improving product quality, manufacturability, and reliability at low cost. Taguchi's introduction of this method in 1980 to several major American industries resulted in significant quality improvement in product and manufacturing process design. While the robust design objective of making product performance insensitive to hard-to-control noise was recognized to be very important, many of the statistical methods proposed by Taguchi, such as the use of signal-to-noise ratios, orthogonal arrays, linear graphs, and accumulation analysis, have room for improvement. To popularize me use of robust design among engineers, it is essential to develop more effective, statistically efficient, and user-friendly tech niques and tools. This paper first summarizes the statistical methods for planning and analyzing robust design experiments originally proposed by Taguchi; then reviews newly developed statistical methods and identifies areas and problems where more resear...

An overview of taguchi method and newly developed statistical methods for robust design

Summary Fractional factorial (FF) designs with minimum aberration are often regarded as the best designs and are commonly used in practice. There are, however, situations in which other designs can meet practical needs better. A catalogue of designs would make it easy to search for 'best' designs according to various criteria. By exploring the algebraic structure of the FF designs, we propose an algorithm for constructing complete sets of FF designs. A collection of FF designs with 16, 27, 32 and 64 runs is given.

/pdf/a-catalogue-of-two-level-and-three-level-fractional-wb7p5s8xex.pdf

A catalogue of two-level and three-level fractional factorial designs with small runs

The simulation community has used metarnodels to study the behavior of computer simulations for over twenty-five years. The most popular teebniques have been based on parametric polynomial response surface approximations. In this state of the art review, we present recent developments in this area. We also discuss seven alternative modeling strategies that are active topics in the current literature.

/pdf/metamodels-for-simulation-input-output-relations-3cv8bf1geq.pdf

Metamodels for simulation input-output relations

Full Factorial Designs- to Full Factorial Designs with Two-Level Factors- Analysis of Full Factorial Experiments- Common Randomization Restrictions- More Full Factorial Design Examples- Fractional Factorial Designs- Fractional Factorial Designs: The Basics- Fractional Factorial Designs for Estimating Main Effects- Designs for Estimating Main Effects and Some Two-Factor Interactions- Resolution V Fractional Factorial Designs- Augmenting Fractional Factorial Designs- Fractional Factorial Designs with Randomization Restrictions- More Fractional Factorial Design Examples- Additional Topics- Response Surface Methods and Second-Order Designs- Special Topics Regarding the Design- Special Topics Regarding the Analysis- Appendices and Tables- Upper Percentiles of t Distributions, t- Upper Percentiles of F Distributions, F- Upper Percentiles for Lenth t Statistics, and- Computing Upper Percentiles for Maximum Studentized Residual- Orthogonal Blocking for Full 2 Factorial Designs- Column Labels of Generators for Regular Fractional Factorial Designs- Tables of Minimum Aberration Regular Fractional Factorial Designs- Minimum Aberration Blocking Schemes for Fractional Factorial Designs- Alias Matrix Derivation- Distinguishing Among Fractional Factorial Designs

/pdf/a-comprehensive-guide-to-factorial-two-level-experimentation-4jnkvwmoo0.pdf

A Comprehensive Guide to Factorial Two-Level Experimentation

In planning a fractional factorial experiment prior knowledge may suggest that some interactions are potentially important and should therefore be estimated free of the main effects. In this article, we propose a graph-aided method to solve this problem for two-level experiments. First, we choose the defining relations for a 2 n–k design according to a goodness criterion such as the minimum aberration criterion. Then we construct all of the nonisomorphic graphs that represent the solutions to the problem of simultaneous estimation of main effects and two-factor interactions for the given defining relations. In each graph a vertex represents a factor and an edge represents the interaction between the two factors. For the experiment planner, the job is simple: Draw a graph representing the specified interactions and compare it with the list of graphs obtained previously. Our approach is a substantial improvement over Taguchi's linear graphs.

Youyi Chen

Papers

A graph-aided method for planning two-level experiments when certain interactions are important