Raymond H. Myers

Taguchi's parameter design: a panel discussion

It is more than a decade since Genichi Taguchis 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 Taguchis 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 Taguchis methodology for implementing it. The topics covered include the importance of vari...

Combining Taguchi and Response Surface Philosophies: A Dual Response Approach

G. Taguchi and his school have made significant advances in the use of experimental design and analysis in industry. Of particular significance is their promotion of the use of statistical methods ...

Response surface methodology: 1966–1988

Response sarfxe methodology (RSM) is a collection of tools developed in the 1950s for the purpose of determining optimum operating conditions in applications in the chemical industry. This article reviews the progrrss of RSM in the general areas of experimental design and analysis and indicates how its role has been affected by advanccs in other fields of applied statistics. Current areas of research in RSM are highlighted. and areas for future research are discussed.

Response Surface Methodology: A Retrospective and Literature Survey

Response surface methodology (RSM) is a collection of statistical design and numerical optimization techniques used to optimize processes and product designs. The original work in this area dates from the 1950s and has been widely used, especially in the chemical and process industries. The last 15 years have seen the widespread application of RSM and many new developments. In this review paper we focus on RSM activities since 1989. We discuss current areas of research and mention some areas for future research.

Response Surface Alternatives to the Taguchi Robust Parameter Design Approach

Abstract This department publishes articles of interest to statistical practitioners. Innovative applications of known methodology may be suitable, but sizable case studies should be submitted to other journals. Brief descriptions and illustrations of new developments that are potentially useful in statistical practice are appropriate. Acceptable articles should appeal to a substantial number of practitioners. In the decade of the 1980s much attention was given to the data and analytic and experimental design efforts of Genichi Taguchi. Methodology advocated by Taguchi, often called robust parameter design, gained the interest of practitioners working in industry in quality improvement. Many statisticians in the West have pointed out apparent flaws in the Taguchi approach. As the controversy surrounding Taguchi matures, several investigators have embraced important aspects of parameter design and the result is a collection of alternatives to the Taguchi approach. Some of these alternatives highlight the u...

Response Surface Techniques for Dual Response Systems

The purpose of this paper is to present the theory and develop an algorithm associated with the exploration of a dual response surface system. The approach is to find conditions on a set of independent or “design” variables which maximize (or minimize) a “primary response” function subject to the condition that a “constraint response” function takes on some specified or desirable value. A method is outlined whereby a user can generate simple two dimensional plots to determine the conditions of constrained maximum primary response regardless of the number of independent variables in the system. He thus is able to reduce to simple plotting the complex task of exploring the dual response system. The procedure that is used to generate the plots depends on the nature of the individual univariate response functions. In certain situations it becomes necessary to apply the additional constraint that the located operating conditions are a certain “distance” from the origin of the independent variables (or the cent...

Response Surface Methodology—Current Status and Future Directions

This paper is a reflection on where response surface methodology (RSM) is at this point and what will likely be future directions. The emphasis in the last two decades on robust parameter design has brought attention to RSM as an alternative methodology..

Response Surface methods for Bi-Randomization Structures

Cost control, resource availability, and/or difficulty in performing complete randomizations may dictate the necessity to run response surface experiments in a bi-randomization error control format of which the split-plot design is a special case. Respo..

A TUTORIAL ON GENERALIZED LINEAR MODELS

Situations in which the observations are not normally distributed arise frequently in the quality engineering field. The standard approach to the analysis of such responses is to transform the response into a new quantity that behaves more like a normal..

Fraction of Design Space to Assess Prediction Capability of Response Surface Designs

Variance Dispersion Graphs (VDGs) are useful summaries for comparing competing designs on a fixed design space. However, they do not give all useful information about the prediction capability of a design. We propose the Fraction of Design Space (FDS) technique, which addresses some of the shortcomings of VDGs. The new technique gives the researcher more detailed information by quantifying the fraction of design space where the scaled prediction variance (SPV) is less than or equal to any pre-specified value. The FDS graph gives the researcher information about the distribution of the SPV in the region based on the ranges and proportions of possible SPV values. Several variations on the graph, including plotting the variance of the estimated mean response, are also presented to allow for specialized consideration of different designs. The FDS technique complements the use of VDGs to give the researcher more insight into the prediction capability of a design. Several standard designs with different numbers of factors are studied with both methods.