References
- Nocedal, J.; Wright, S. J. Numerical Optimization; Springer: New York, NY, 1999.
- Mirjalili, S.; Dong, J. S.; Lewis, A. Genetic Algorithm: Theory, Literature Review, and Application in Image Reconstruction. In Nature-Inspired Optimizers; Mirjalili, S., Song Dong, J. Lewis, A., Eds.; Springer: Cham, 2020; Vol. 811, pp. 69–85.
- Simon, D. Evolutionary Optimization Algorithms. John Wiley & Sons, 2013.
- Li, J. Y.; Zhan, Z. H.; Zhang, J. Evolutionary Computation for Expensive Optimization: A Survey. Mach Intell Res. 2022, 19(1), 3–23. DOI: 10.1007/s11633-022-1317-4.
- Dogan, A.; Birant, D. Machine Learning and Data Mining in Manufacturing. Expert Syst. Appl. 2021, 166, 114060. DOI: 10.1016/j.eswa.2020.114060.
- Weichert, D.; Link, P.; Stoll, A.; Rüping, S.; Ihlenfeldt, S.; Wrobel, S. A Review of Machine Learning for the Optimization of Production Processes. Int. J. Adv. Manuf. Technol. 2019, 104(5), 1889–1902. DOI: 10.1007/s00170-019-03988-5.
- Pfrommer, J.; Zimmerling, C.; Liu, J.; Kärger, L.; Henning, F.; Beyerer, J. Optimisation of Manufacturing Process Parameters Using Deep Neural Networks as Surrogate Models. Procedia CIRP. 2018, 72, 426–431. DOI: 10.1016/j.procir.2018.03.046.
- Vohra, M.; Nath, P.; Mahadevan, S.; Lee, Y. T. T. Fast Surrogate Modeling Using Dimensionality Reduction in Model Inputs and Field Output: Application to Additive Manufacturing. Reliab Eng Syst. 2020, 201, 106986. DOI: 10.1016/j.ress.2020.106986.
- Verma, D.; Dong, Y.; Sharma, M.; Chaudhary, A. K. Advanced Processing of 3D Printed Biocomposite Materials Using Artificial Intelligence. Mater. Manuf. Processes. 2022, 37(5), 518–538. DOI: 10.1080/10426914.2021.1945090.
- Dang, X. P. Constrained Multi-Objective Optimization of EDM Process Parameters Using Kriging Model and Particle Swarm Algorithm. Mater. Manuf. Processes. 2018, 33(4), 397–404. DOI: 10.1080/10426914.2017.1292037.
- Jha, R.; Patra, P. K.; Srivastava, A. K. AI-Guided Optimization of Manufacturing Protocols for AHSS Coils. Mater. Manuf. Processes. 2022, 38(2), 1–8. DOI: 10.1080/10426914.2022.2105871.
- Miriyala, S. S.; Mitra, K. Novel Sample Size Determination Methods for Parsimonious Training of Black Box Models. In Indian Control Conference (ICC). Guwahati, India: IEEE. 2017. pp. 39–46
- Miriyala, S. S.; Subramanian, V. R.; Mitra, K. TRANSFORM-ANN for Online Optimization of Complex Industrial Processes: Casting Process as Case Study. Eur. J. Oper. Res. 2018, 264(1), 294–309. DOI: 10.1016/j.ejor.2017.05.026.
- Pantula, P. D.; Miriyala, S. S.; Mitra, K. KERNEL: Enabler to Build Smart Surrogates for Online Optimization and Knowledge Discovery. Matter. Manuf. Processes. 2017, 32(10), 1162–1171. DOI: 10.1080/10426914.2016.1269918.
- Breitenbach, J.; Seidenspinner, F.; Vural, F.; Beisswanger, P.; Buettner, R. A Systematic Literature Review of Machine Learning Approaches for Optimization in Additive Manufacturing. In 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), Los Alamitos, CA, USA, June 2022, IEEE, pp. 1147–1152. DOI: 10.1109/COMPSAC54236.2022.00180.
- Yang, S.; Navarathna, P.; Ghosh, S.; Bequette, B. W. Hybrid Modeling in the Era of Smart Manufacturing. Comput. Chem. Eng. 2020, 140, 106874. DOI: 10.1016/j.compchemeng.2020.106874.
- Bárkányi, Á.; Chován, T.; Németh, S.; Abonyi, J. Modelling for Digital Twins—potential Role of Surrogate Models. Processes. 2021, 9(3), 476. DOI: 10.3390/pr9030476.
- Suthaharan, S. Big Data Analytics: Machine Learning and Bayesian Learning Perspectives—what is Done? What is Not? Wiley Interdiscip Rev Data Min Knowl Discov. 2019, 9(1), e1283. DOI: 10.1002/widm.1283.
- Shahriari, B.; Swersky, K.; Wang, Z.; Adams, R. P.; De Freitas, N. Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proc. IEEE. 2015, 104(1), 148–175. DOI: 10.1109/JPROC.2015.2494218.
- Attia, P. M.; Grover, A.; Jin, N.; Severson, K. A.; Markov, T. M.; Liao, Y. H.; Chen, M. H.; Cheong, B.; Perkins, N.; Yang, Z., et al. Closed-Loop Optimization of Fast-Charging Protocols for Batteries with Machine Learning. Nature. 2020, 578(7795), 397–402.
- Zhang, Y.; Apley, D. W.; Chen, W. Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variables. Sci. Rep. 2020, 10(1), 1–13. DOI: 10.1038/s41598-020-60652-9.
- Korovina, K.; Xu, S.; Kandasamy, K.; Neiswanger, W.; Poczos, B.; Schneider, J.; Xing, E. C.: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations. In AISTATS, Proceedings of Machine Learning Research (PMLR), June 2020, 108, 3393–3403.
- Griffiths, R. R.; Hernández-Lobato, J. M. Constrained Bayesian Optimization for Automatic Chemical Design Using Variational Autoencoders. Chem. Sci. 2020, 11(2), 577–586. DOI: 10.1039/C9SC04026A.
- Shin, D.; Cupertino, A.; de Jong, M. H.; Steeneken, P. G.; Bessa, M. A.; Norte, R. A. Spiderweb Nanomechanical Resonators via Bayesian Optimization: Inspired by Nature and Guided by Machine Learning. Adv. Mater. 2022, 34(3), 2106248. DOI: 10.1002/adma.202106248.
- Iwama, R.; Kaneko, H. Design of Ethylene Oxide Production Process Based on Adaptive Design of Experiments and Bayesian Optimization. J Adv Manuf Process. 2021, 3(3), e10085. DOI: 10.1002/amp2.10085.
- AlBahar, A.; Kim, I.; Yue, X. A Robust Asymmetric Kernel Function for Bayesian Optimization, with Application to Image Defect Detection in Manufacturing Systems. IEEE Trans on Autom Sci Eng. 2021, 19(4), 3222–3233. DOI: 10.1109/TASE.2021.3114157.
- Maurya, A. Bayesian Optimization for Predicting Rare Internal Failures in Manufacturing Processes. In 2016 IEEE international conference on big data (big data), Washington D.C., USA, December 2016, IEEE, pp. 2036–2045. DOI: 10.1109/BigData38862.2016.
- Shu, L.; Jiang, P.; Shao, X.; Wang, Y. A New Multi-Objective Bayesian Optimization Formulation with the Acquisition Function for Convergence and Diversity. J Mech Des. 2020, 142(9), 091703. DOI: 10.1115/1.4046508.
- Hanaoka, K. Comparison of Conceptually Different Multi-Objective Bayesian Optimization Methods for Material Design Problems. Mater. Today Commun. 2022, 31, 103440. DOI: 10.1016/j.mtcomm.2022.103440.
- Daulton, S.; Balandat, M.; Bakshy, E. Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization. Advances in Neural Information Processing Systems, NeurIPS Proceedings, Dec. 2020, 33, 9851–9864.
- Mogilicharla, A.; Chugh, T.; Majumdar, S.; Mitra, K. Multi-Objective Optimization of Bulk Vinyl Acetate Polymerization with Branching. Mater. Manuf. Processes. 2014, 29(2), 210–217. DOI: 10.1080/10426914.2013.872271.
- Deb, K. Multi-Objective Optimisation Using Evolutionary Algorithms: An Introduction; Springer: London, 2011.
- Balandat, M.; Karrer, B.; Jiang, D.; Daulton, S.; Letham, B.; Wilson, A. G.; Bakshy, E. BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization. Advances in neural information processing systems, NeurIPS Proceedings, Dec. 2020, 33.