Article2004

Response Surface Methodology: A Retrospective and Literature Survey

Raymond H. Myers · Douglas C. Montgomery · G. Geoffrey Vining · Connie M. Borror · Scott M. Kowalski

706 citations0/0 files storedJournal of Quality Technology

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Optimal Experimental Design Methods Advanced Optimization Algorithms Research Advanced Multi-Objective Optimization Algorithms

Abstract

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.

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References

1996Response Surface Methodology: Process and Product Optimization Using Designed Experiments 2000A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code 1989Design and Analysis of Computer Experiments 1988Empirical Model Building and Response Surfaces 1980Simultaneous Optimization of Several Response Variables 1951On the Experimental Attainment of Optimum Conditions 1991Quality Engineering Using Robust Design 1992On the Experimental Attainment of Optimum Conditions

Cited by

2009Response-Surface Methods in R , Using rsm 2020Bayesian Optimization for Adaptive Experimental Design: A Review 2009Response Surface Methodology 2008Response surface design evaluation and comparison 2019Response Surface Methodology 2007Multiple predictor smoothing methods for sensitivity analysis: Description of techniques 2008Practical applications of design of experiments in the field of engineering: a bibliographical review

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