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Developing New Processes and Optimizing Performance Using Response Surface Methodology

Authors: S. Raissi

Abstract:

Response surface methodology (RSM) is a very efficient tool to provide a good practical insight into developing new process and optimizing them. This methodology could help engineers to raise a mathematical model to represent the behavior of system as a convincing function of process parameters. Through this paper the sequential nature of the RSM surveyed for process engineers and its relationship to design of experiments (DOE), regression analysis and robust design reviewed. The proposed four-step procedure in two different phases could help system analyst to resolve the parameter design problem involving responses. In order to check accuracy of the designed model, residual analysis and prediction error sum of squares (PRESS) described. It is believed that the proposed procedure in this study can resolve a complex parameter design problem with one or more responses. It can be applied to those areas where there are large data sets and a number of responses are to be optimized simultaneously. In addition, the proposed procedure is relatively simple and can be implemented easily by using ready-made standard statistical packages.

Keywords: Response Surface Methodology (RSM), Design of Experiments (DOE), Process modeling, Process setting, Process optimization.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1057147

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References:


[1] D.C. Montgomery, Design and Analysis of Experiments, 3rd ed., John Wiley & Sons, New York, 1991, pp. 270-569.
[2] NIST/SEMATECH e-Handbook of Statistical Methods, 2005. http://www.itl.nist.gov/div898/handbook/.
[3] Theodore T. Allen, Introduction to Engineering Statistics and Six Sigma - Statistical Quality Control and Design of Experiments and Systems, London: Springer-Verlag, 2006, pp 241.
[4] J.M. Carr, E.A. McCracken, Statistical program planning for process development, Chemical Engineering Progress. Vol. 56, no. 11, 1960, pp. 56-61.
[5] E.E. Lind, J. Goldin, J.B. Hickman, Fitting yield and cost response surfaces, Chemical Engineering Progress. Vol. 56, no. 11, 1960, pp. 62- 68.
[6] C.-P. Xu, S.-W. Kim, H.-J. Hwang, J.-W. Yun, Application of statistically based experimental designs for the optimization of exopolysaccharide production by Cordyceps milltaris NG3, Biotechnol. Appl. Biochem, vol. 36, 2002, pp. 127-131.
[7] M.J. Anderson, H.P. Anderson, Applying DOE to microwave popcorn, Process Ind. Quality, 1993, pp. 30-32.
[8] S.E. Kruger, I.C. Silva, J.M.A. Rebello, Factorial design of experiments applied to reliability assessment in discontinuity mapping by ultrasound, NDT Net, vol. 3, no. 11, 1998.
[9] B.V. Mehta, H. Ghulman, R. Gerth, Extrusion die design: a new methodology of using design of experiments as a precursor to neural networks, JOM-e , vol. 51, no. 9, 1999.
[10] Jae-Seob Kwak, Application of Taguchi and response surface methodologies for geometric error in surface grinding process, International Journal of Machine Tools & Manufacture, vol.45, 2005, pp. 327-334
[11] Myers, R.H.; Carter, W.H., Jr. Response Surface Techniques for Dual Response Systems. Technometrics 1973, vol. 15, no. 2, pp. 301-317.
[12] Harrington, E.C., Jr. The Desirability Function. Industrial Quality Control, 1965, vol. 21, no. 10, pp. 494 - 498.
[13] Derringer, G.; Suich, R. Simultaneous Optimization of Several Response Variables. Journal of Quality. Technology, 1980, vol. 12, no. 4, pp. 214 -219.