Abstract
In manufacturing process optimization, analyzing a large volume of operational data is getting attention due to the development of data processing techniques. One of important issues in the process optimization is a simultaneous optimization of mean and variance of a response variable. It is called dual response optimization (DRO). Traditional DRO methods build statistical models for the mean and variance of the response variable by fitting the models to experimental data. Then, an optimal setting of input variables is obtained by analyzing the fitted models. This model based approach assumes that the statistical model is fitted well to the data. However, it is often difficult to satisfy this assumption when dealing with a large volume of operational data from manufacturing line. In such a case, data mining approach is an attractive alternative. We proposes a particular data mining method by modifying patient rule induction method for DRO. The proposed method obtains an optimal setting of the input variables directly from the operational data where mean and variance are optimized. We explain a detailed procedure of the proposed method with case examples.
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References
Myers, R.H., Montgomery, D.C., Anderson-Cook, C.M.: Response Surface Methodology. Wiley, Hoboken (2009)
Lee, D., Jeong, I., Kim, K.: A posterior preference articulation approach to dual-response surface optimization. IIE Trans. 42(2), 161–171 (2010)
Lee, D., Kim, K.: Interactive weighting of bias and variance in dual response surface optimization. Expert Syst. Appl. 39(5), 5900–5906 (2012)
Lee, H., Lee, D.: A solution selection approach to multiresponse surface optimization based on a clustering method. Qual. Eng. 28(4), 388–401 (2016)
Xu, D., Albin, S.: Robust optimization of experimentally derived objective functions. IIE Trans. 35, 793–802 (2003)
Friedman, J., Fisher, N.: Bump hunting in high-dimensional data. Stat. Comput. -LONDON- 9(2), 123–142 (1999)
Chong, I., Albin, S., Jun, C.: A data mining approach to process optimization without an explicit quality function. IIE Trans. 39, 795–804 (2007)
Lee, M., Kim, K.: MR-PRIM: patient rule induction method for multiresponse optimization. Qual. Eng. 20(2), 232–242 (2008)
Myers, R., Carter, W.: Response surface methods for dual response systems. Technometrics 15(2), 301–307 (1973)
Vining, G., Myers, R.: Combining Taguchi and response surface philosophies: a dual response approach. J. Qual. Technol. 22(1), 38–45 (1990)
Lin, D., Tu, W.: Dual response surface optimization. J. Qual. Technol. 27(1), 34–39 (1995)
Ding, R., Lin, D., Wei, D.: Dual-response surface optimization: a weighted MSE approach. Qual. Eng. 16(3), 377–385 (2004)
Jeong, I., Kim, K., Chang, S.: Optimal weighting of bias and variance in dual response surface optimization. J. Qual. Technol. 37(3), 236–247 (2005)
Yeh, I.: Modeling of strength of high performance concrete using artificial neural networks. Cem. Concr. Res. 28(12), 1797–1808 (1998)
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Lee, DH. (2017). Data Mining Approach to Dual Response Optimization. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10404. Springer, Cham. https://doi.org/10.1007/978-3-319-62392-4_34
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DOI: https://doi.org/10.1007/978-3-319-62392-4_34
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