Integrated parameter and tolerance design based on a multivariate Gaussian process model

References

• Alshraideh, H., and E. Del Castillo. 2014. “Gaussian Process Modeling and Optimization of Profile Response Experiments.” Quality and Reliability Engineering International 30 (4): 449–462.
   Web of Science ®Google Scholar
• Ankenman, B., B. L. Nelson, and J. Staum. 2010. “Stochastic Kriging for Simulation Metamodeling.” Operations Research 58 (2): 371–382.
   Web of Science ®Google Scholar
• Chen, J., and Y. Tang. 2013. “Sequential Algorithms for Structural Design Optimization Under Tolerance Conditions.” Engineering Optimization 46 (9): 1183–1199.
   Web of Science ®Google Scholar
• Chiang, Y. M., and H. H. Hsieh. 2009. “The Use of the Taguchi Method with Grey Relational Analysis to Optimize the Thin-Film Sputtering Process with Multiple Quality Characteristic in Color Filter Manufacturing.” Computers & Industrial Engineering 56 (2): 648–661. doi:10.1016/j.cie.2007.12.020.
   Web of Science ®Google Scholar
• Cho, B. R., Y. J. Kim, D. L. Kimbler, and M. D. Phillips. 2000. “An Integrated Joint Optimization Procedure for Robust and Tolerance Design.” International Journal of Production Research 38 (10): 2309–2325.
   Web of Science ®Google Scholar
• Colosimo, B. M., and N. Senin, eds. 2010. Geometric Tolerances: Impact on Product Design, Quality Inspection and Statistical Process Monitoring. London: Springer-Verlag/Springer eBooks. doi:10.1007/978-1-84996-311-4.
   Google Scholar
• Costa, N. R., and J. Lourenço. 2016. “Gaussian Process Model — An Exploratory Study in the Response Surface Methodology.” Quality and Reliability Engineering International 32 (7): 2367–2380.
   Web of Science ®Google Scholar
• Del Castillo, E., S.-K. S. Fan, and J. Semple. 1997. “The Computation of Global Optima in Dual Response Systems.” Journal of Quality Technology 29 (3): 347–353.
   Web of Science ®Google Scholar
• Del Castillo, E., S.-K. S. Fan, and J. Semple. 1999. “Optimization of Dual Response Systems: A Comprehensive Procedure for Degenerate and Nondegenerate Problems.” European Journal of Operational Research 112 (1): 174–186.
   Web of Science ®Google Scholar
• Ding, R., D. K. J. Lin, and D. Wei. 2004. “Dual-Response Surface Optimization: A Weighted MSE Approach.” Quality Engineering 16 (3): 377–385.
   Google Scholar
• Dürichen, R., T. Wissel, F. Ernst, M. A. F. Pimentel, D. A. Clifton, and A. Schweikard. 2014. “A Unified Approach for Respiratory Motion Prediction and Correlation with Multi-Task Gaussian Processes.” In Proceedings of the 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). Piscataway, NJ: IEEE. doi:10.1109/MLSP.2014.6958895.
   Google Scholar
• Fan, S.-K. S. 2000. “A Generalized Global Optimization Algorithm for Dual Response Systems.” Journal of Quality Technology 32 (4): 444–456.
   Web of Science ®Google Scholar
• Fan, S.-K. S., and E. Del Castillo. 1999. “Calculation of An Optimal Region of Operation for Dual Response Systems Fitted From Experimental Data.” Journal of the Operational Research Society 50 (8): 826–836.
   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. doi:10.1080/00207543.2020.1740344.
   PubMed Web of Science ®Google Scholar
• Han, M., X. Liu, M. Huang, and M. H. Y. Tan. 2019. “Integrated Parameter and Tolerance Optimization of a Centrifugal Compressor Based on a Complex Simulator.” Journal of Quality Technology. doi:10.1080/00224065.2019.1611358.
   Web of Science ®Google Scholar
• Han, M., and M. H. Y. Tan. 2016. “Integrated Parameter and Tolerance Design with Computer Experiments.” IIE Transactions 48 (11): 1004–1015.
   Web of Science ®Google Scholar
• Hazrati-Marangaloo, H., and H. Shahriari. 2017. “A Novel Approach to Simultaneous Robust Design of Product Parameters and Tolerances Using Quality Loss and Multivariate ANOVA Concepts.” Quality and Reliability Engineering International 33 (1): 71–85.
   Web of Science ®Google Scholar
• He, Z., P. F. Zhu, and S. H. Park. 2012. “A Robust Desirability Function Method for Multi-Response Surface Optimization Considering Model Uncertainty.” European Journal of Operational Research 221 (1): 241–247.
   Web of Science ®Google Scholar
• Huang, H., D. K. J. Lin, M. Q. Liu, and J. F. Yang. 2016. “Computer Experiments with Both Qualitative and Quantitative Variables.” Technometrics 58 (4): 495–507.
   Web of Science ®Google Scholar
• Jeang, A. 2010. “Robust Computer-Aided Parameter and Tolerance Determination for An Electronic Circuit Design.” International Journal of Production Research 41 (5): 883–895.
   Web of Science ®Google Scholar
• Jin, Y., K. Wang, T. Yu, and M. Fang. 2008. “Reliable Multi-Objective Optimization of High-Speed WEDM Process Based on Gaussian Process Regression.” International Journal of Machine Tools and Manufacture 48 (1): 47–60.
   Web of Science ®Google Scholar
• Kim, K. J., and D. K. J. Lin. 1998. “Dual Response Surface Optimization: A Fuzzy Modeling Approach.” Journal of Quality Technology 30 (1): 1–10.
   Web of Science ®Google Scholar
• Kleijnen, J. P. C. 2017. “Regression and Kriging Metamodels with Their Experimental Designs in Simulation: A Review.” European Journal of Operational Research 256 (1): 1–16.
   Web of Science ®Google Scholar
• Kleijnen, J. P. C., and E. Mehdad. 2014. “Multivariate Versus Univariate Kriging Metamodels for Multi-Response Simulation Models.” European Journal of Operational Research 236 (2): 573–582.
   Web of Science ®Google Scholar
• Ko, Y. H., K. J. Kim, and C. H. Jun. 2005. “A New Loss Function-Based Method for Multiresponse Optimization.” Journal of Quality Technology 37 (1): 50–59.
   Web of Science ®Google Scholar
• Kuo, Y., T. Yang, and G. W. Huang. 2008. “The Use of a Grey-Based Taguchi Method for Optimizing Multi-Response Simulation Problems.” Engineering Optimization 40 (6): 517–528.
   Web of Science ®Google Scholar
• Li, W., L. Gao, and M. Xiao. 2020. “Multidisciplinary Robust Design Optimization Under Parameter and Model Uncertainties.” Engineering Optimization 52 (3): 426–445. doi:10.1080/0305215X.2019.1590564.
   Web of Science ®Google Scholar
• Li, W., and C. F. J. Wu. 1999. “An Integrated Method of Parameter Design and Tolerance Design.” Quality Engineering 11 (3): 417–425.
   Google Scholar
• Li, Y., and Q. Zhou. 2016. “Pairwise Meta-Modeling of Multivariate Output Computer Models Using Nonseparable Covariance Function.” Technometrics 58 (4): 483–494.
   Web of Science ®Google Scholar
• Li, Y., Q. Zhou, X. Huang, and L. Zeng. 2017. “Pairwise Estimation of Multivariate Gaussian Process Models with Replicated Observations: Application to Multivariate Profile Monitoring.” Technometrics 60 (1): 70–78.
   Web of Science ®Google Scholar
• Liu, S. F., and Y. Lin. 2017. Introduction to Grey Systems Modeling Software. Berlin: Springer.
   Google Scholar
• Mehdad, E., and J. P. C. Kleijnen. 2015. “Classic Kriging Versus Kriging with Bootstrapping Or Conditional Simulation: Classic Kriging's Robust Confidence Intervals and Optimization.” Journal of the Operational Research Society 66 (11): 1804–1814.
   Web of Science ®Google Scholar
• Montgomery, D. C. 2017. Design and Analysis of Experiments. Hoboken, NJ: Wiley.
   Google Scholar
• Moskowitz, H., R. Plante, and J. Duffy. 2001. “Multivariate Tolerance Design Using Quality Loss.” IIE Transactions 33 (6): 437–448.
   Web of Science ®Google Scholar
• Myers, R. H., D. C. Montgomery, G. G. Vining, C. M. Borror, and S. M. Kowalski. 2014. “Response Surface Methodology: A Retrospective and Literature Survey.” Journal of Quality Technology 36 (1): 53–77.
   Web of Science ®Google Scholar
• Ouyang, L., J. Chen, Y. Ma, C. Park, and J. Jin. 2020. “Bayesian Closed-Loop Robust Process Design Considering Model Uncertainty and Data Quality.” IISE Transactions 52 (3): 288–300.
   Web of Science ®Google Scholar
• Ouyang, L., Y. Ma, J. Wang, and Y. Tu. 2017a. “A New Loss Function for Multi-Response Optimization with Model Parameter Uncertainty and Implementation Errors.” European Journal of Operational Research 258 (2): 552–563.
   Web of Science ®Google Scholar
• Ouyang, L., Y. Ma, J. Wang, Y. Tu, and J. H. Byun. 2017b. “An Interval Programming Model for Continuous Improvement in Micro-Manufacturing.” Engineering Optimization 50 (3): 400–414.
   Web of Science ®Google Scholar
• Ouyang, L., D. Zhou, C. Park, J. Chen, and Y. Tu. 2018. “Ensemble Modelling Technique for a Micro-Drilling Process Based on a Two-Stage Bootstrap.” Engineering Optimization 51 (3): 503–519.
   Web of Science ®Google Scholar
• Ozdemir, A., and B. R. Cho. 2016. “A Nonlinear Integer Programming Approach to Solving the Robust Parameter Design Optimization Problem.” Quality and Reliability Engineering International 32 (8): 2859–2870.
   Web of Science ®Google Scholar
• Ozdemir, A., and B. R. Cho. 2017. “Response Surface-Based Robust Parameter Design Optimization with Both Qualitative and Quantitative Variables.” Engineering Optimization 49 (10): 1796–1812.
   Web of Science ®Google Scholar
• Parussini, L., D. Venturi, P. Perdikaris, and G. E. Karniadakis. 2017. “Multi-Fidelity Gaussian Process Regression for Prediction of Random Fields.” Journal of Computational Physics 336: 36–50.
   Web of Science ®Google Scholar
• Pignatiello, J. J. 1993. “Strategies for Robust Multiresponse Quality Engineering.” IIE Transactions 25 (3): 5–15.
   Web of Science ®Google Scholar
• Qian, P. Z. G., H. Wu, and C. F. J. Wu. 2008. “Gaussian Process Models for Computer Experiments with Qualitative and Quantitative Factors.” Technometrics 50 (3): 383–396.
   Web of Science ®Google Scholar
• Rajasekera, J. R., and S. C. Fang. 1995. “A New Approach to Tolerance Allocation in Design Cost Analysis.” Engineering Optimization 24 (4): 283–291.
   Web of Science ®Google Scholar
• Rebonato, R., and P. Jäckel. 1999. “The Most General Methodology to Create a Valid Correlation Matrix for Risk Management and Option Pricing Purposes.” Social Science Electronic Publishing 32 (5): 1–11.
   Google Scholar
• Rougier, J. 2008. “Efficient Emulators for Multivariate Deterministic Functions.” Journal of Computational and Graphical Statistics 17 (4): 827–843.
   Web of Science ®Google Scholar
• Santner, T. J., B. J. Williams, and W. I. Notz. 2003. The Design and Analysis of Computer Experiments. 2nd ed. New York: Springer. doi:10.1007/978-1-4939-8847-1.
   Google Scholar
• Shin, S., and B. R. Cho. 2008. “Development of a Sequential Optimization Procedure for Robust Design and Tolerance Design Within a Bi-Objective Paradigm.” Engineering Optimization 40 (11): 989–1009.
   Web of Science ®Google Scholar
• Svenson, J. D., and T. J. Santner. 2010. “Multiobjective Optimization of Expensive Black-Box Functions via Expected Maximin Improvement.” pp. 1–21. The Ohio State University, Columbus. https://www.semanticscholar.org/paper/Multiobjective-Optimization-of-Expensive-Black-Box-Svenson-Santner/5b31ac4b54dc3c5c3050e44aa25c568ca7a8bbe7.
   Google Scholar
• Tan, M. H. Y. 2015. “Stochastic Polynomial Interpolation for Uncertainty Quantification with Computer Experiments.” Technometrics 57 (4): 457–467.
   Web of Science ®Google Scholar
• Tan, M. H. Y., and C. F. J. Wu. 2012. “Robust Design Optimization with Quadratic Loss Derived From Gaussian Process Models.” Technometrics 54 (1): 51–63.
   Web of Science ®Google Scholar
• Tsai, J. T. 2010. “Robust Optimal-Parameter Design Approach for Tolerance Design Problems.” Engineering Optimization 42 (12): 1079–1093.
   Web of Science ®Google Scholar
• Tsui, K. L. 1992. “An Overview of Taguchi Method and Newly Developed Statistical Methods for Robust Design.” IIE Transactions 24 (5): 44–57.
   Web of Science ®Google Scholar
• Wang, P., and M. Liang. 2005. “An Integrated Approach to Tolerance Synthesis, Process Selection and Machining Parameter Optimization Problems.” International Journal of Production Research 43 (11): 2237–2262.
   Web of Science ®Google Scholar
• Williams, C. K., and C. E. Rasmussen. 2006. Gaussian Processes for Machine Learning. Cambridge: MIT Press.
   Google Scholar
• Wu, C. F. J., and M. S. Hamada. 2009. Experiments: Planning, Analysis and Parameter Design Optimization. New York: Wiley.
   Google Scholar
• Xiong, X., S. Li, and F. Wu. 2020. “Robust Parameter Design for Nonlinear Signal-Response Systems Using Kriging Models.” Engineering Optimization 52 (8): 1344–1361. doi:10.1080/0305215X.2019.1650924.
   Web of Science ®Google Scholar
• Zhou, Q., P. Z. G. Qian, and S. Zhou. 2011. “A Simple Approach to Emulation for Computer Models with Qualitative and Quantitative Factors.” Technometrics 53 (3): 266–273.
   Web of Science ®Google Scholar
• Zhou, H., Q. Zhou, C. Liu, and T. Zhou. 2018. “A Kriging Metamodel-Assisted Robust Optimization Method Based on a Reverse Model.” Engineering Optimization 50 (2): 253–272.
   Web of Science ®Google Scholar