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http://dx.doi.org/10.7469/JKSQM.2018.46.1.039

A literature review on RSM-based robust parameter design (RPD): Experimental design, estimation modeling, and optimization methods  

Le, Tuan-Ho (Department of Technology and Technique, Quy Nhon University)
Shin, Sangmun (Department of Industrial & Management Systems Engineering, Dong-A University)
Publication Information
Journal of Korean Society for Quality Management / v.46, no.1, 2018 , pp. 39-74 More about this Journal
Abstract
Purpose: For more than 30 years, robust parameter design (RPD), which attempts to minimize the process bias (i.e., deviation between the mean and the target) and its variability simultaneously, has received consistent attention from researchers in academia and industry. Based on Taguchi's philosophy, a number of RPD methodologies have been developed to improve the quality of products and processes. The primary purpose of this paper is to review and discuss existing RPD methodologies in terms of the three sequential RPD procedures of experimental design, parameter estimation, and optimization. Methods: This literature study composes three review aspects including experimental design, estimation modeling, and optimization methods. Results: To analyze the benefits and weaknesses of conventional RPD methods and investigate the requirements of future research, we first analyze a variety of experimental formats associated with input control and noise factors, output responses and replication, and estimation approaches. Secondly, existing estimation methods are categorized according to their implementation of least-squares, maximum likelihood estimation, generalized linear models, Bayesian techniques, or the response surface methodology. Thirdly, optimization models for single and multiple responses problems are analyzed within their historical and functional framework. Conclusion: This study identifies the current RPD foundations and unresolved problems, including ample discussion of further directions of study.
Keywords
Robust Design; Design of Experiments; Response Surface Methodology; Parameter Estimation; Optimization.;
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