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http://dx.doi.org/10.7736/KSPE.2013.30.10.1079

Optimum Design based on Sequential Design of Experiments and Artificial Neural Network for Heat Resistant Characteristics Enhancement in Front Pillar Trim  

Lee, Jung Hwan (Global Research and Development Center, Mando Corporation)
Suh, Myung Won (School of Mechanical Engineering, Sungkyunkwan University)
Publication Information
Journal of the Korean Society for Precision Engineering / v.30, no.10, 2013 , pp. 1079-1086 More about this Journal
Abstract
Optimal mount position of a front pillar trim considering heat resistant characteristics can be determined by two methods. One is conventional approximate optimization method which uses the statistical design of experiments (DOE) and response surface method (RSM). Generally, approximated optimum results are obtained through the iterative process by a trial and error. The quality of results depends seriously on the factors and levels assigned by a designer. The other is a methodology derived from previous work by the authors, which is called sequential design of experiments (SDOE), to reduce a trial and error procedure and to find an appropriate condition for using artificial neural network (ANN) systematically. An appropriate condition is determined from the iterative process based on the analysis of means. With this new technique and ANN, it is possible to find an optimum design accurately and efficiently.
Keywords
Sequential Design of Experiments(SDOE); Artificial Neural Network(ANN); Response Surface Method(RSM); Design of Experiments(DOE);
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Times Cited By KSCI : 2  (Citation Analysis)
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