An online approach for robust parameter design with incremental Gaussian process

References

• Aye, S. A., and P. S. Heyns. 2018. Prognostics of slow speed bearings using a composite integrated Gaussian process regression model. International Journal of Production Research 56 (14):4860–73. doi:10.1080/00207543.2018.1470340.
   Web of Science ®Google Scholar
• Box, G. 1990. Commentary: [communications between statisticians and engineers/physical scientists]. Technometrics 32 (3):251–2. doi:10.2307/1269094.
   Google Scholar
• Box, G., and K. Wilson. 1951. On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society 13 (1):1–45.
   Google Scholar
• Byun, J.-H., and C.-H. Kim. 2014. A simplex evolutionary operation for mixture production processes. Quality Engineering 26 (4):383–91. doi:10.1080/08982112.2013.830741.
   Web of Science ®Google Scholar
• Cheng, H., and A. Sandu. 2010. Collocation least-squares polynomial chaos method. In Proceedings of the 2010 Spring Simulation Multiconference, pp. 1–6. doi:10.1145/1878537.1878621.
   Google Scholar
• Dasgupta, T. 2007. Robust parameter design for automatically controlled systems and nanostructure synthesis. Doctoral dissertation, Georgia Institute of Technology.
   Google Scholar
• Dette, H., and A. Pepelyshev. 2010. Generalized Latin hypercube design for computer experiments. Technometrics 52 (4):421–9. doi:10.1198/TECH.2010.09157.
   Web of Science ®Google Scholar
• Feng, Z., J. Wang, Y. Ma, and Y. Tu. 2020. Robust parameter design based on Gaussian process with model uncertainty. International Journal of Production Research 59 (9):2772–88. doi:10.1080/00207543.2020.1740344.
   Web of Science ®Google Scholar
• Jin, J., and Y. Ding. 2004. Online automatic process control using observable noise factors for discrete-part manufacturing. IIE Transactions 36 (9):899–911. doi:10.1080/07408170490473105.
   Web of Science ®Google Scholar
• Joseph, V. R. 2003. Robust parameter design with feed-forward control. Technometrics 45 (4):284–92. doi:10.1198/004017003000000113.
   Web of Science ®Google Scholar
• Kontar, R., S. Y. Zhou, and J. Horst. 2016. Estimation and monitoring of key performance indicators of manufacturing systems using the multi-output Gaussian process. International Journal of Production Research 55 (8):2304–19. doi:10.1080/00207543.2016.1237791.
   Web of Science ®Google Scholar
• Lin, D. K., and W. Tu. 1995. Dual response surface optimization. Journal of Quality Technology 27 (1):34–9. doi:10.1080/00224065.1995.11979556.
   Web of Science ®Google Scholar
• Mohammed, R. O., and G. C. Cawley. 2017. Over-fitting in model selection with Gaussian process regression. In International Conference on Machine Learning and Data Mining in Pattern Recognition, 192–205. Cham: Springer.
   Google Scholar
• Montgomery, D. C. 2017. Design and analysis of experiments. Hoboken, NJ: Wiley.
   Google Scholar
• Myers, R. H., D. C. Montgomery, and C. M. Anderson-Cook. 2016. Response surface methodology: Process and product optimization using designed experiments. Hoboken, NJ: Wiley.
   Google Scholar
• Neal, R. M. 2012. Bayesian learning for neural networks. Vol. 118. New York: Springer.
   Google Scholar
• Ouyang, L., Y. Ma, J. H. Byun, J. Wang, and Y. Tu. 2016. An interval approach to robust design with parameter uncertainty. International Journal of Production Research 54 (11):3201–15. doi:10.1080/00207543.2015.1078920.
   Web of Science ®Google Scholar
• Petelin, D., and J. Kocijan. 2011, April. Control system with evolving Gaussian process models. In 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS), 178–184. IEEE.
   Google Scholar
• Quaglio, M., C. Waldron, A. Pankajakshan, E. Cao, A. Gavriilidis, E. S. Fraga, and F. Galvanin. 2019. An online reparametrisation approach for robust parameter estimation in automated model identification platforms. Computers & Chemical Engineering 124:270–84. doi:10.1016/j.compchemeng.2019.01.010.
   Web of Science ®Google Scholar
• Robinson, T. J., C. M. Borror, and R. H. Myers. 2004. Robust parameter design: A review. Quality and Reliability Engineering International 20 (1):81–101. doi:10.1002/qre.602.
   Web of Science ®Google Scholar
• Shaibu, A. B., and B. R. Cho. 2009. Another view of dual response surface modelling and optimization in robust parameter design. The International Journal of Advanced Manufacturing Technology 41 (7–8):631–41. doi:10.1007/s00170-008-1509-2.
   Web of Science ®Google Scholar
• Taguchi, G. 1986. Introduction to quality engineering: Designing quality into products and processes (No. 658.562 T3).
   Google Scholar
• Vanli, O. A., and E. Del Castillo. 2009. Bayesian approaches for on-line robust parameter design. IIE Transactions 41 (4):359–71. doi:10.1080/07408170802108534.
   Web of Science ®Google Scholar
• Vanli, O. A., C. Zhang, and B. Wang. 2013. An adaptive Bayesian approach for robust parameter design with observable time series noise factors. IIE Transactions 45 (4):374–90. doi:10.1080/0740817X.2012.689123.
   Web of Science ®Google Scholar
• Vining, G. G., and L. L. Bohn. 1998. Response surfaces for the mean and variance using a nonparametric approach. Journal of Quality Technology 30 (3):282–91. doi:10.1080/00224065.1998.11979856.
   Web of Science ®Google Scholar
• Welch, W. J. 1985. ACED: Algorithms for the construction of experimental designs. The American Statistician 39 (2):146. doi:10.2307/2682827.
   Google Scholar
• Williams, C. K I., and C. E. Rasmussen. 2006. Gaussian process for machine learning. Cambridge: MIT Press.
   Google Scholar
• Yadav, O. P., G. Thambidorai, B. Nepal, and L. Monplaisir. 2014. A robust framework for multi‐response surface optimization methodology. Quality and Reliability Engineering International 30 (2):301–11. doi:10.1002/qre.1499.
   Web of Science ®Google Scholar
• Zhou, X., and T. Jiang. 2016. Metamodel selection based on stepwise regression. Structural and Multidisciplinary Optimization 54 (3):641–57. doi:10.1007/s00158-016-1442-1.
   Web of Science ®Google Scholar
• Zhou, X., T. Jiang, Z. Zhou, and X. Hu. 2021. Sequential∊-support vector regression based online robust parameter design. Computers & Industrial Engineering 158:107391. doi:10.1016/j.cie.2021.107391.
   Web of Science ®Google Scholar
• Zhou, X., D. K. Lin, X. Hu, and L. Ouyang. 2019. Sequential Latin hypercube design with both space‐filling and projective properties. Quality and Reliability Engineering International 35 (6):1941–51. doi:10.1002/qre.2485.
   Web of Science ®Google Scholar
• Zhou, X. J., Y. Z. Ma, and X. F. Li. 2011. Ensemble of surrogates with recursive arithmetic average. Structural and Multidisciplinary Optimization 44 (5):651–71. doi:10.1007/s00158-011-0655-6.
   Web of Science ®Google Scholar
• Zhou, X., Y. Ma, Y. Tu, and Y. Feng. 2013. Ensemble of surrogates for dual response surface modelling in robust parameter design. Quality and Reliability Engineering International 29 (2):173–97. doi:10.1002/qre.1298.
   Web of Science ®Google Scholar