Developing New Processes and Optimizing Performance Using Response Surface Methodology

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.

Authors:

• S. Raissi

Keywords:

• Response Surface Methodology (RSM)
• Design of Experiments (DOE)
• Process modeling
• Process setting
• Process optimization.

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.