Estimating Rubber Covered Conveyor Belting Cure Times Using Multiple Simultaneous Optimizations Ensemble
DOI:
https://doi.org/10.31181/oresta180222016pKeywords:
Multiresponse surface methodology, ensembling, credibility of results, solution uncertainty, small sample size problems, simultaneous optimisationAbstract
Multiple response surface methodology (MRSM) has been the favorite method for optimizing multiple response processes though it has two weaknesses which challenge the credibility of its solutions. The first weakness is the use of experimentally generated small sample size datasets, and the second is the selection, using classical model selection criteria, of single best models for each response for use in simultaneous optimization to obtain the optimum or desired solution. Classical model selection criteria do not always agree on the best model resulting in model uncertainty. The selection of single best models for each response for simultaneous optimization loses information in rejected models. This work proposes the use of multiple simultaneous optimizations to estimate multiple solutions that are ensembled in solving a conveyor belting cure time problem. The solution is compared with one obtained by simultaneous optimization of single best models for each response. The two results were different. However, results show that it is possible to obtain a more credible solution through ensembling of solutions from multiple simultaneous optimizations.
Downloads
References
Ahangi, A., Langroudi, A. F., Yazdanpanah, F., & Mirroshandel, S. A. (2019). A novel fusion mixture of active experts algorithm for traffic signs recognition. Multimedia Tools and Applications, 78(14), 20217-20237. https://doi.org/10.1007/s11042-019-7391-0
Alhorn, K., Schorning, K., & Dette, H. (2019). Optimal designs for frequentist model averaging. Biometrika, 106(3), 665-682. https://doi.org/10.1093/biomet/asz036
Burnham, K., Anderson, D. (2002). Model selection and multi-model inference. A practical information-theoretic approach, Springer, New York [Chapter 1; Chapter 4]
Geman, S., Bienenstock, E., & Doursat, R. (1992). Neural networks and the bias/variance dilemma. Neural computation, 4(1), 1-58. https://doi.org/10.1162/neco.1992.4.1.1
Gatos, K. G., & Karger-Kocsis, J. (2004). Estimation of the vulcanization time for rubber by considering their linear viscoelastic response assessed by a plate-plate rheometer. Kautschuk Gummi Kunststoffe, 57(7-8), 350-354.
Gough, J. (2017). Calculation of times and temperatures for press vulcanization of thick rubber pads. Rubber Chemistry and Technology, 90(1), 89-107. https://doi.org/10.5254/rct.16.83774
Hejazi, T.H., Seyyed-Esfahani, M., Antony, J. (2017) “A New Methodology based on Multistage Stochastic Programming for Quality Chain Design Problem”, International Journal of Industrial Engineering: Theory, Applications and Practice, 24(1), 12-31.
Jenkins D.G., Quintana-Ascencio P.F. (2020). A solution to minimum sample size for regressions. PLoS ONE 15(2): e0229345. https://doi.org/10.1371/journal.pone.0229345
Karaağaç, B., İnal, M., & Deniz, V. (2012). Predicting optimum cure time of rubber compounds by means of ANFIS. Materials & Design, 35, 833-838. https://doi.org/10.1016/j.matdes.2011.03.062
Khuri, A. I. (2017). A general overview of response surface methodology. Biometrics & Biostatistics International Journal, 5(3), 87-93. DOI: 10.15406/bbij.2017.05.00133
Kittler, J., (1998). “On combining classifiers,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(3), 226–239.
Mäkelä, M. (2017). Experimental design and response surface methodology in energy applications: A tutorial review. Energy Conversion and Management, 151, 630-640. https://doi.org/10.1016/j.enconman.2017.09.021
Myers, R.H., Montgomery, D.C., Anderson-Cook, C.M, (2016). Response Surface Methodology: Process and Product Optimisation Using Designed Experiments, 4th Edition, ISBN: 978-1-118-91601-8. [Chapter 6]
Domingo Pavolo, D. C. (2020). Determining Cure Time of Rubber-Covered Mining Conveyor Belts using Multi response Surface Methodology. International journal of operations and quantitative management, 26(1), 29-48.
Polikar, R., & Polikar, R. (2006). Ensemble based systems in decision making. IEEE Circuit Syst. Mag. 6, 21–45 (2006).
Rawlings, J.O., Pantula, G.S., Dickey A.D. 1998, Applied Regression Analysis: A Research Tool, Second Edition, Springer-Verlag New York Inc.
Ueda, N., & Nakano, R. (1996, June). Generalization error of ensemble estimators. In Proceedings of International Conference on Neural Networks (ICNN'96) (Vol. 1, pp. 90-95). IEEE. https://doi.org/10.1109/ICNN.1996.548872
Xu, Y., & Goodacre, R. (2018). On splitting training and validation set: a comparative study of cross-validation, bootstrap and systematic sampling for estimating the generalization performance of supervised learning. Journal of analysis and testing, 2(3), 249-262. https://doi.org/10.1007/s41664-018-0068-2
Yang, P., Hwa Yang, Y., B Zhou, B., & Y Zomaya, A. (2010). A review of ensemble methods in bioinformatics. Current Bioinformatics, 5(4), 296-308.
Yuan, Z., & Yang, Y. (2005). Combining linear regression models: When and how?. Journal of the American Statistical Association, 100(472), 1202-1214. https://doi.org/10.1198/016214505000000088
