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Finding Cost-Effective Mixtures Robust to Noise Variables in Mixture-Process Experiments
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 Title & Authors
Finding Cost-Effective Mixtures Robust to Noise Variables in Mixture-Process Experiments
Lim, Yong B.;
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 Abstract
In mixture experiments with process variables, we consider the case that some of process variables are either uncontrollable or hard to control, which are called noise variables. Given the such mixture experimental data with process variables, first we study how to search for candidate models. Good candidate models are screened by the sequential variables selection method and checking the residual plots for the validity of the model assumption. Two methods, which use numerical optimization methods proposed by Derringer and Suich (1980) and minimization of the weighted expected loss, are proposed to find a cost-effective robust optimal condition in which the performance of the mean as well as the variance of the response for each of the candidate models is well-behaved under the cost restriction of the mixture. The proposed methods are illustrated with the well known fish patties texture example described by Cornell (2002).
 Keywords
Robust optimal condition for the several combined models;multiple responses surface methods;weighted expected loss;
 Language
English
 Cited by
 References
1.
Cornell, J. A. (2002). Experiments with Mixtures, 3rd ed., Wiley, New York.

2.
Derringer, G. and Suich, R. (1980). Simultaneous optimization of several response variables, Journal of Quality and Technology, 12, 214-219

3.
Goldfarb, H. B., Borror, C. M. and Montgomery, D. C. (2003). Mixture-process variable experiments with noise variables, Journal of Quality Technology, 35, 393-405.

4.
Myers, R. H., Montgomery, D. C. and Anderson-Cook, C. M. (2009). Response Surface Methodology, 3rd ed., Wiley, New York.

5.
Steiner, S. H. and Hamada, M. (1997). Making mixtures robust to noise and mixing measurement variables, Journal of Quality and Technology, 29, 441-450.