Ensemble modelling technique for a micro-drilling process based on a two-stage bootstrap

References

• Aich, U., and S. Banerjee. 2014. “Modeling of EDM Responses by Support Vector Machine Regression with Parameters Selected by Particle Swarm Optimization.” Applied Mathematical Modelling 38 (11): 2800–2818. doi: 10.1016/j.apm.2013.10.073
   Web of Science ®Google Scholar
• Boylan, G. L., and B. R. Cho. 2013. “Robust Parameter Design in Embedded High-Variability Production Processes: an Alternative Approach to Mitigating Sources of Variability.” International Journal of Production Research 51 (15): 4517–4538. doi: 10.1080/00207543.2013.774497
   Web of Science ®Google Scholar
• Chang, G., and Y. L. Tu. 2012. “Closed-loop Control in Ultrafast Laser Milling Process Using Laser Triggered Plasma.” International Journal of Machine Tools and Manufacture 60 (9): 35–39. doi: 10.1016/j.ijmachtools.2012.04.002
   Web of Science ®Google Scholar
• Chou, C.-J., and L.-F. Chen. 2012. “Combining Neural Networks and Genetic Algorithms for Optimising the Parameter Design of the Inter-metal Dielectric Process.” International Journal of Production Research 50 (7): 1905–1916. doi: 10.1080/00207543.2011.574499
   Web of Science ®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. doi: 10.1002/qre.1940
   Web of Science ®Google Scholar
• Efron, B., and R. J. Tibshirani. 1994. An Introduction to the Bootstrap. Boca Raton, FL: CRC Press.
   Google Scholar
• Elsayed, K., and C. Lacor. 2014. “Robust Parameter Design Optimization Using Kriging, RBF and RBFNN with Gradient-Based and Evolutionary Optimization Techniques.” Applied Mathematics and Computation 236 (6): 325–344. doi: 10.1016/j.amc.2014.03.082
   Google Scholar
• Goel, T., R. T. Haftka, W. Shyy, and N. V. Queipo. 2007. “Ensemble of Surrogates.” Structural and Multidisciplinary Optimization 33 (3): 199–216. doi: 10.1007/s00158-006-0051-9
   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. doi: 10.1080/0740817X.2016.1167289
   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. doi: 10.1016/j.ejor.2012.03.009
   Web of Science ®Google Scholar
• Jiang, C., X. Han, G. R. Liu, and G. P. Liu. 2008. “A Nonlinear Interval Number Programming Method for Uncertain Optimization Problems.” European Journal of Operational Research 188 (1): 1–13. doi: 10.1016/j.ejor.2007.03.031
   Web of Science ®Google Scholar
• Jiang, C., Z. G. Zhang, Q. F. Zhang, X. Han, H. C. Xie, and J. Liu. 2014. “A New Nonlinear Interval Programming Method for Uncertain Problems with Dependent Interval Variables.” European Journal of Operational Research 238 (1): 245–253. doi: 10.1016/j.ejor.2014.03.029
   Web of Science ®Google Scholar
• Jin, R., and X. Deng. 2015. “Ensemble Modeling for Data Fusion in Manufacturing Process Scale-Up.” IIE Transactions 47 (3): 203–214. doi: 10.1080/0740817X.2014.916580
   Web of Science ®Google Scholar
• Kim, T., H. Park, and S. Rhee. 2005. “Optimization of Welding Parameters for Resistance Spot Welding of TRIP Steel with Response Surface Methodology.” International Journal of Production Research 43 (21): 4643–4657. doi: 10.1080/00207540500137365
   Web of Science ®Google Scholar
• Köksoy, O., and N. Doganaksoy. 2003. “Joint Optimization of Mean and Standard Deviation Using Response Surface Methods.” Journal of Quality Technology 35 (3): 239–252. doi: 10.1080/00224065.2003.11980218
   Web of Science ®Google Scholar
• Köksoy, O., and S.-K. S. Fan. 2012. “An Upside-Down Normal Loss Function-Based Method for Quality Improvement.” Engineering Optimization 44 (8): 935–945. doi: 10.1080/0305215X.2011.620101
   Web of Science ®Google Scholar
• Kovach, J., and B. R. Cho. 2008. “Development of a Multidisciplinary–Multiresponse Robust Design Optimization Model.” Engineering Optimization 40 (9): 805–819. doi: 10.1080/03052150802046304
   Web of Science ®Google Scholar
• Lin, D. K. J., and W. Tu. 1995. “Dual Response Surface Optimization.” Journal of Quality Technology 27 (1): 34–39. doi: 10.1080/00224065.1995.11979556
   Web of Science ®Google Scholar
• Liu, F., W.-G. Zhang, and L.-H. Zhang. 2014. “Consistency Analysis of Triangular Fuzzy Reciprocal Preference Relations.” European Journal of Operational Research 235 (3): 718–726. doi: 10.1016/j.ejor.2013.10.039
   Web of Science ®Google Scholar
• Mays, J. E., J. B. Birch, and B. Alden Starnes. 2001. “Model Robust Regression: Combining Parametric, Nonparametric, and Semiparametric Methods.” Journal of Nonparametric Statistics 13 (2): 245–277. doi: 10.1080/10485250108832852
   Web of Science ®Google Scholar
• Müller, J., and C. A. Shoemaker. 2014. “Influence of Ensemble Surrogate Models and Sampling Strategy on the Solution Quality of Algorithms for Computationally Expensive Black-Box Global Optimization Problems.” Journal of Global Optimization 60 (2): 123–144. doi: 10.1007/s10898-014-0184-0
   Web of Science ®Google Scholar
• Ng, S. H. 2010. “A Bayesian Model-Averaging Approach for Multiple-Response Optimization.” Journal of Quality Technology 42 (1): 52–68. doi: 10.1080/00224065.2010.11917806
   Web of Science ®Google Scholar
• Ouyang, L. H., Y. Z. Ma, J. X. Chen, Z. G. Zeng, and Y. L. Tu. 2016. “Robust Optimization of Nd:YLF Laser Beam Micro-drilling Process Using Bayesian Probabilistic Approach.” International Journal of Production Research 54 (21): 6644–6659. doi: 10.1080/00207543.2016.1154212
   Web of Science ®Google Scholar
• Ouyang, L. H., Y. Z. Ma, J. J. Wang, and Y. L. Tu. 2017. “A New Loss Function for Multi-response Optimization with Model Parameter Uncertainty and Implementation Errors.” European Journal of Operational Research 258 (2): 552–563. doi: 10.1016/j.ejor.2016.09.045
   Web of Science ®Google Scholar
• Ouyang, L. H., Y. Z. Ma, J. J. Wang, Y. L. Tu, and J.-H. Byun. 2018. “An Interval Programming Model for Continuous Improvement in Micro-manufacturing.” Engineering Optimization 50 (3): 400–414. doi: 10.1080/0305215X.2017.1317765
   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. doi: 10.1080/0305215X.2016.1271881
   Web of Science ®Google Scholar
• Peterson, J. J. 2004. “A Posterior Predictive Approach to Multiple Response Surface Optimization.” Journal of Quality Technology 36 (2): 139–153. doi: 10.1080/00224065.2004.11980261
   Web of Science ®Google Scholar
• Pickle, S. M., T. J. Robinson, J. B. Birch, and C. M. Anderson-Cook. 2008. “A Semi-parametric Approach to Robust Parameter Design.” Journal of Statistical Planning and Inference 138 (1): 114–131. doi: 10.1016/j.jspi.2007.05.018
   Web of Science ®Google Scholar
• Rajagopal, R., and E. del Castillo. 2005. “Model-Robust Process Optimization Using Bayesian Model Averaging.” Technometrics 47 (2): 152–163. doi: 10.1198/004017005000000120
   Web of Science ®Google Scholar
• Robinson, T. J., J. B. Birch, and B. A. Starnes. 2010. “A Semi-parametric Approach to Dual Modeling When No Replication Exists.” Journal of Statistical Planning and Inference 140 (10): 2860–2869. doi: 10.1016/j.jspi.2010.03.009
   Web of Science ®Google Scholar
• Vijayan, K., P. Ranjithkumar, and B. Shanmugarajan. 2018. “Comparison of Response Surface Methodology and Genetic Algorithm in Parameter Optimization of Laser Welding Process.” Applied Mathematics and Information Sciences 12 (1): 239–248. doi: 10.18576/amis/120124
   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): 282–291. doi: 10.1080/00224065.1998.11979856
   Web of Science ®Google Scholar
• Wan, W., and J. B. Birch. 2011. “A Semiparametric Technique for the Multi-response Optimization Problem.” Quality and Reliability Engineering International 27 (1): 47–59. doi: 10.1002/qre.1106
   Web of Science ®Google Scholar
• Wang, G. D., Z. He, L. Xue, Q. A. Cui, S. S. Lv, and P. P. Zhou. 2017. “Bootstrap Analysis of Designed Experiments for Reliability Improvement with a Non-constant Scale Parameter.” Reliability Engineering & System Safety 160 (4): 114–121. doi: 10.1016/j.ress.2016.12.006
   Google Scholar
• Wang, J. J., Y. Z. Ma, L. H. Ouyang, and Y. L. Tu. 2016. “A New Bayesian Approach to Multi-response Surface Optimization Integrating Loss Function with Posterior Probability.” European Journal of Operational Research 249 (1): 231–237. doi: 10.1016/j.ejor.2015.08.033
   Web of Science ®Google Scholar
• Yanıkoğlu, İ., D. den Hertog, and J. P. C. Kleijnen. 2016. “Robust Dual-Response Optimization.” IIE Transactions 48 (3): 298–312. doi: 10.1080/0740817X.2015.1067737
   Web of Science ®Google Scholar
• Yu, J. C., C. K. Chang, and Suprayitno. 2017. “Robust Design Optimisation via Surrogate Network Model and Soft Outer Array Design.” International Journal of Production Research, doi:doi: 10.1080/00207543.2017.1356484.
   PubMed Web of Science ®Google Scholar
• Zhou, X. J., and T. Jiang. 2016. “Metamodel Selection Based on Stepwise Regression.” Structural and Multidisciplinary Optimization 54 (3): 641–657. doi: 10.1007/s00158-016-1442-1
   Web of Science ®Google Scholar
• Zhou, X. J., Y. Z. Ma, Y. L. Tu, and Y. Feng. 2013. “Ensemble of Surrogates for Dual Response Surface Modeling in Robust Parameter Design.” Quality and Reliability Engineering International 29 (2): 173–197. doi: 10.1002/qre.1298
   Web of Science ®Google Scholar