 Investigating estimation error reduction strategies in complex engineering systemsPaul Goethals and Byung Rae Cho International Journal of Data Analysis Techniques and Strategies, 2014, vol. 6, issue 1, 43-72 Abstract: When the manufacturing objective is process or product improvement, quality practitioners will frequently resort to one or more approaches within the broader class of response surface methodology. Several techniques, such as the dual response, robust parameter design, and desirability function approach, may be effective tools to solve the multi-response optimisation problem. All of these techniques are designed to identify the factor settings that lead to an optimal solution in terms of the mean or variance among characteristics. The skewness in the distribution of observations for one or more characteristics, however, is not considered. The techniques also traditionally rely on the fit of second-order response surface designs in estimating each response, which may be unreliable in some cases. In contrast, this paper offers an approach to solving complex multi-response optimisation problems that considers both the error associated with process skewness and the accuracy of a response surface. Keywords: response surface methodology; RSM; robust design; estimators; variability; coefficient of variation; multi-response optimisation; process skewness; estimation error reduction; complex engineering systems. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:injdan:v:6:y:2014:i:1:p:43-72 Access Statistics for this article More articles in International Journal of Data Analysis Techniques and Strategies from Inderscience Enterprises Ltd |
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