Page 50 - ESAM-1-4

Engineering Science in
Additive Manufacturing Machine learning for biomedical metal AM

doi: 10.1007/s40194-023-01604-5 doi: 10.1007/s41230-024-3145-3
89. Zhan Z, Hu W, Meng Q. Data-driven fatigue life prediction 99. Liu D, Wang Y. Metal additive manufacturing process design
in additive manufactured titanium alloy: A damage based on physics constrained neural networks and multi-
mechanics based machine learning framework. Eng Fract objective Bayesian optimization. Manuf Lett. 2022;33:817-827.
Mechan. 2021;252:107850.
doi: 10.1016/j.mfglet.2022.07.101
doi: 10.1016/j.engfracmech.2021.107850
100. Ma J, Cao B, Dong S, et al. MLMD: A programming-free
90. Zhang M, Sun CN, Zhang X, et al. High cycle fatigue life AI platform to predict and design materials. NPJ Computat
prediction of laser additive manufactured stainless steel: Mater. 2024;10(1):59.
A machine learning approach. Int J Fatigue. 2019;128:105194.
doi: 10.1038/s41524-024-01243-4
doi: 10.1016/j.ijfatigue.2019.105194
101. Hou Yi C, Jianzhao W, Xinzhi W, Wentao Y. Process
91. Shen T, Zhang W, Li B. Machine learning-enabled predictions parameter optimization of metal additive manufacturing:
of as-built relative density and high-cycle fatigue life of A review and outlook. J Mater Inform. 2022;2(4):16.
Ti6Al4V alloy additively manufactured by laser powder bed
fusion. Mater Today Commun. 2023;37:107286. doi: 10.20517/jmi.2022.18
doi: 10.1016/j.mtcomm.2023.107286 102. Grbcic L, Müller J, de Jong WA. Efficient inverse design
optimization through multi-fidelity simulations, machine
92. Tang YT, Panwisawas C, Ghoussoub JN, et al. Alloys- learning, and boundary refinement strategies. Eng Comput.
by-design: Application to new superalloys for additive 2024;40(6):4081-4108.
manufacturing. Acta Mater. 2021;202:417-436.
doi: 10.1007/s00366-024-02053-4
doi: 10.1016/j.actamat.2020.09.023
103. Hua Y, Jin Y, Hao K, Cao Y. Generating multiple reference
93. Wang L, Zhang Y, Chia HY, Yan W. Mechanism of keyhole vectors for a class of many-objective optimization problems
pore formation in metal additive manufacturing. NPJ with degenerate Pareto fronts. Complex Intell Syst.
Computat Mater. 2022;8(1):22. 2020;6(2):275-285.
doi: 10.1038/s41524-022-00699-6 doi: 10.1007/s40747-020-00136-5
94. Lee JA, Sagong MJ, Jung J, Kim ES, Kim HS. Explainable 104. Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and
machine learning for understanding and predicting elitist multiobjective genetic algorithm: NSGA-II. IEEE
geometry and defect types in Fe-Ni alloys fabricated by Transactions on Evolutionary Computation. 2002;6(2):182-
laser metal deposition additive manufacturing. J Mater Res 197. doi:10.1109/4235.996017
Technol. 2023;22:413-423.
105. Wang J, Feng L, Xu J, et al. Optimal process parameter
doi: 10.1016/j.jmrt.2022.11.137 combinations search for desired deposited layer geometry
95. Gui Y, Aoyagi K, Bian H, Chiba A. Detection, classification in laser-arc hybrid additive manufacturing based on multi-
and prediction of internal defects from surface morphology pass overlapping deposited layer contour prediction model
data of metal parts fabricated by powder bed fusion type and improved NSGA-II algorithm. Optics Laser Technol.
additive manufacturing using an electron beam. Addit 2025;187:112700.
Manuf. 2022;54:102736. doi: 10.1016/j.optlastec.2025.112700
doi: 10.1016/j.addma.2022.102736 106. Padhye N, Deb K. Multi‐objective optimisation and
96. Du Y, Mukherjee T, DebRoy T. Physics-informed multi‐criteria decision making in SLS using evolutionary
machine learning and mechanistic modeling of additive approaches. Rapid Prototyp J. 2011;17(6):458-478.
manufacturing to reduce defects. Appl Mater Today. doi: 10.1108/13552541111184198
2021;24:101123.
107. Aboutaleb AM, Mahtabi MJ, Tschopp MA, Bian L. Multi-
doi: 10.1016/j.apmt.2021.101123 objective accelerated process optimization of mechanical
97. Dharmadhikari S, Menon N, Basak A. A reinforcement properties in laser-based additive manufacturing: Case
learning approach for process parameter optimization in study on Selective Laser Melting (SLM) Ti-6Al-4V. J Manuf
additive manufacturing. Addit Manuf. 2023;71:103556. Process. 2019;38:432-444.
doi: 10.1016/j.addma.2023.103556 doi: 10.1016/j.jmapro.2018.12.040
98. Zhou HR, Yang H, Li HQ, et al. Advancements in machine 108. Startt J, McCarthy MJ, Wood MA, Donegan S, Dingreville R.
learning for material design and process optimization Bayesian blacksmithing: Discovering thermomechanical
in the field of additive manufacturing. China Foundry. properties and deformation mechanisms in high-entropy
2024;21(2):101-115. refractory alloys. NPJ Computat Mater. 2024;10(1):164.

Volume 1 Issue 4 (2025) 28 doi: 10.36922/ESAM025440031