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Engineering Science in
Additive Manufacturing Machine learning for biomedical metal AM

entropy alloys. Virtual Phys Prototyp. 2025;20(1):e2524524. learning to investigate the composition-microstructure-
mechanical property relationships in titanium alloys.
doi: 10.1080/17452759.2025.2524524
J Mater Process Technol. 2023;311:117800.
68. Gor M, Dobriyal A, Wankhede V, et al. Density prediction
in powder bed fusion additive manufacturing: Machine doi: 10.1016/j.jmatprotec.2022.117800
learning-based techniques. Appl Sci. 2022;12(14):7271. 79. Calvat M, Bean C, Anjaria D, et al. Learning metal
doi: 10.3390/app12147271 microstructural heterogeneity through spatial mapping
of diffraction latent space features. NPJ Computat Mater.
69. Chan KS, Koike M, Mason RL, Okabe T. Fatigue life of 2025;11(1):284.
titanium alloys fabricated by additive layer manufacturing
techniques for dental implants. Metallurgical Mater Trans A. doi: 10.1038/s41524-025-01770-8
2013;44(2):1010-1022. 80. Chi J, Huang X, He D, et al. Obtaining strength and ductility
doi: 10.1007/s11661-012-1470-4 synergy for directed energy deposited Ti17 alloys by
machine learning. Mater Lett. 2024;356:135537.
70. Dawood HI, Mohammed KS, Rahmat A, Uday MB. The
influence of the surface roughness on the microstructures doi: 10.1016/j.matlet.2023.135537
and mechanical properties of 6061 aluminium alloy using 81. Wang H, Li B, Zhang W, Xuan F. Microstructural feature-
friction stir welding. Surf Coat Technol. 2015;270:272-283. driven machine learning for predicting mechanical
doi: 10.1016/j.surfcoat.2015.02.045 tensile strength of laser powder bed fusion (L-PBF)
additively manufactured Ti6Al4V alloy. Eng Fract Mech.
71. Jiang X, Lu J, Zhao N, Chen Z, Zhao Z. A review of wear 2024;295:109788.
in additive manufacturing: Wear mechanism, materials, and
process. Lubricants. 2024;12(9):321. doi: 10.1016/j.engfracmech.2023.109788
doi: 10.3390/lubricants12090321 82. Liu S, Stebner AP, Kappes BB, Zhang X. Machine learning
for knowledge transfer across multiple metals additive
72. Pegues J, Roach M, Scott Williamson R, Shamsaei N. Surface manufacturing printers. Addit Manuf. 2021;39:101877.
roughness effects on the fatigue strength of additively
manufactured Ti-6Al-4V. Int J Fatigue. 2018;116:543-552. doi: 10.1016/j.addma.2021.101877
doi: 10.1016/j.ijfatigue.2018.07.013 83. Fang L, Cheng L, Glerum JA, Bennett J, Cao J, Wagner GJ.
Data-driven analysis of process, structure, and properties
73. Koo J, Park E, Baek AMC, Kim N. The Research of Surface of additively manufactured Inconel 718 thin walls. NPJ
Roughness Prediction with Machine Learning According Computat Mater. 2022;8(1):126.
to Process Parameters in Laser Powder Bed Fusion. Berlin:
Springer Singapore; 2022. p. 62-65. doi: 10.1038/s41524-022-00808-5
74. Xia C, Pan Z, Polden J, Li H, Xu Y, Chen S. Modelling 84. Liu YT, Chua C, Soh V, Sun Z, Chua CK, Sing SL. Revealing
and prediction of surface roughness in wire arc additive the underlying mechanism in controlling Young’s modulus of
manufacturing using machine learning. J Intell Manuf. additively manufactured Ti-6Al-4V using fuzzified machine
2022;33(5):1467-1482. learning. Virtual Phys Prototyp. 2025;20(1):e2443103.
doi: 10.1007/s10845-020-01725-4 doi: 10.1080/17452759.2024.2443103
75. So MS, Seo GJ, Kim DB, Shin JH. Prediction of metal 85. Dong S, Wang Y, Li J, Li Y, Wang L, Zhang J. Machine learning
additively manufactured surface roughness using deep aided prediction and design for the mechanical properties of
neural network. Sensors. 2022;22(20):7955. magnesium alloys. Metals Mater Int. 2024;30(3):593-606.
doi: 10.3390/s22207955 doi: 10.1007/s12540-023-01531-6
76. Mukherjee T, Elmer JW, Wei HL, et al. Control of grain 86. Akbari P, Zamani M, Mostafaei A. Machine learning
structure, phases, and defects in additive manufacturing prediction of mechanical properties in metal additive
of high-performance metallic components. Prog Mater Sci. manufacturing. Addit Manuf. 2024;91:104320.
2023;138:101153. doi: 10.1016/j.addma.2024.104320
doi: 10.1016/j.pmatsci.2023.101153 87. Lian Z, Li M, Lu W. Fatigue life prediction of aluminum
77. Yan F, Xiong W, Faierson E. Grain structure control of alloy via knowledge-based machine learning. Int J Fatigue.
additively manufactured metallic materials. Materials. 2022;157:106716.
2017;10(11):1260. doi: 10.1016/j.ijfatigue.2021.106716
doi: 10.3390/ma10111260
88. Johnsen AR, Petersen JE, Pedersen MM, Yıldırım HC. Factors
78. Zhang F, Huang K, Zhao K, et al. Directed energy deposition affecting the fatigue strength of additively manufactured
combining high-throughput technology and machine Ti-6Al-4V parts. Weld World. 2023;68(2):361-409.

Volume 1 Issue 4 (2025) 27 doi: 10.36922/ESAM025440031