DOI QR코드

DOI QR Code

공정변수를 갖는 혼합물 실험 자료의 분석

Analysis of mixture experimental data with process variables

  • 임용빈 (이화여자대학교 자연과학대학 통계학과)
  • Lim, Yong-B. (Department of Statistics, Ewha Womans University)
  • 투고 : 2012.08.17
  • 심사 : 2012.09.14
  • 발행 : 2012.09.30

초록

Purpose: Given the mixture components - process variables experimental data, we propose the strategy to find the proper combined model. Methods: Process variables are factors in an experiment that are not mixture components but could affect the blending properties of the mixture ingredients. For example, the effectiveness of an etching solution which is measured as an etch rate is not only a function of the proportions of the three acids that are combined to form the mixture, but also depends on the temperature of the solution and the agitation rate. Efficient designs for the mixture components - process variables experiments depend on the mixture components - process variables model which is called a combined model. We often use the product model between the canonical polynomial model for the mixture and process variables model as a combined model. Results: First we choose the reasonable starting models among the class of admissible product models and practical combined models suggested by Lim(2011) based on the model selection criteria and then, search for candidate models which are subset models of the starting model by the sequential variables selection method or all possible regressions procedure. Conclusion: Good candidate models are screened by the evaluation of model selection criteria and checking the residual plots for the validity of the model assumption. The strategy to find the proper combined model is illustrated with examples in this paper.

키워드

참고문헌

  1. Lim, Y.(2010), "Mixture response surface methology for improving the current operating condition", Journal of Korean Society for Quality Management, 38, pp. 413-424.
  2. Lim, Y.(2011), "Practical designs for mixture component- process experiments", Journal of Korean Society for Quality Management, 39, pp. 400-411.
  3. Cornell, J. A.(2002), Experiments with Mixtures, 3rd ed., New York: Wiley.
  4. Cornell, J. A. and Gorman, J. W.(1984), "Fractional design plans for process variables in mixture experiments", Journal of Quality Technology, 16, pp. 20-38. https://doi.org/10.1080/00224065.1984.11978883
  5. Kowalski S., Cornell, J. A. and Vining, G. G.(2000), "A new model and class of designs for mixture experiments with process variables", Commun. Statist. -Theory Meth, 29, pp. 2255-2280. https://doi.org/10.1080/03610920008832606
  6. Myers, R. H. and Montgomery, D. C.(2009), Response Surface Methodology, 3rd ed., New York: Wiley.
  7. Nas, T., Fargestad, E. M. and Cornell, J. A.(1998), "A comparison of methods for analyzing data from a three component mixture experiment in the presence of variation created by two process variables", Chemometrics and Intelligent Laboratory Systems, 41, pp. 221-235. https://doi.org/10.1016/S0169-7439(98)00056-2