Page 58 - IJAMD-2-2

International Journal of AI for
Materials and Design ML-driven optimization in additive manufacturing

parameters on geometric and elasto-visco-plastic material 98. Wang X, Liu J, Zhang Y, et al. Advances in precision
properties in vat photopolymerization. Addit Manuf. microfabrication through digital light processing: System
2023;72:103641. development, material and applications. Virtual Phys
Prototyp. 2023;18(1):e2248101.
doi: 10.1016/j.addma.2023.103641
doi: 10.1080/17452759.2023.2248101
88. Andreu A, Su PC, Kim JH, et al. 4D printing materials for
vat photopolymerization. Addit Manuf. 2021;44:102024. 99. Frumosu FD, Méndez Ribó M, Shan S, Zhang Y,
Kulahci M. Online monitoring for error detection in
doi: 10.1016/j.addma.2021.102024
vat photopolymerization. Int J Comput Integr Manuf.
89. Pazhamannil RV, Hadidi HM, Puthumana G. Development 2023;36(9):1313-1330.
of a low‐cost volumetric additive manufacturing printer doi: 10.1080/0951192X.2022.2162600
using less viscous commercial resins. Polym Eng Sci.
2023;63(1):65-77. 100. Cao L, Lu L, Liu X. Waiting time prediction for bottom-up
vat photopolymerization. Addit Manuf. 2023;74:103693.
doi: 10.1002/pen.26186
doi: 10.1016/j.addma.2023.103693
90. Wu H, Chen P, Yan C, Cai C, Shi Y. Four-dimensional
printing of a novel acrylate-based shape memory polymer 101. Wang X, Liu J, Dong R, Gilchrist MD, Zhang N. High-precision
using digital light processing. Mater Des. 2019;171:107704. digital light processing (DLP) printing of microstructures
for microfluidics applications based on a machine learning
doi: 10.1016/j.matdes.2019.107704 approach. Virtual Phys Prototyp. 2024;19(1):e2318774.
91. Hosseinabadi HG, Nieto D, Yousefinejad A, et al. Ink doi: 10.1080/17452759.2024.2318774
material selection and optical design considerations in DLP
3D printing. Appl Mater Today. 2023;30:101721. 102. Pingali R, Saha SK. Printability prediction in projection two-
photon lithography via machine learning based surrogate
doi: 10.1016/j.apmt.2022.101721 modeling of photopolymerization. J Micro Nano-Manuf.
92. Guan J, You S, Xiang Y, et al. Compensating the cell-induced 2022;10(3):031005.
light scattering effect in light-based bioprinting using deep doi: 10.1115/1.4063021
learning. Biofabrication. 2021;14(1):015011.
103. Charalampous P, Kladovasilakis N, Kostavelis I, Tsongas K,
doi: 10.1088/1758-5090/ac3b92 Tzetzis D, Tzovaras D. Machine learning-based mechanical
93. You S, Guan J, Alido J, et al. Mitigating scattering effects behavior optimization of 3D print constructs manufactured via
in light-based three-dimensional printing using machine the FFF process. J Mater Eng Perform. 2022;31(6):4697-4706.
learning. J Manuf Sci Eng. 2020;142(8):081002. doi: 10.1007/s11665-021-06535-0
doi: 10.1115/1.4046986 104. Veeman D, Sudharsan S, Surendhar GJ, Shanmugam R,
94. Zhao L, Zhao Z, Ma L, Li S, Men Z, Wu L. Developing the Guo L. Machine learning model for predicting the hardness
optimized control scheme for digital light processing 3D of additively manufactured acrylonitrile butadiene styrene.
printing by combining numerical simulation and machine Mater Today Commun. 2023;35:106147.
learning-guided temperature prediction. J Manuf Process. doi: 10.1016/j.mtcomm.2023.106147
2024;132:363-374.
105. Sharma S, Gupta V, Mudgal D, Srivastava V. Predicting
doi: 10.1016/j.jmapro.2024.10.049 biomechanical properties of additively manufactured
95. Ma Y, Tian Z, Wang B, et al. Enhancing the 3D printing polydopamine coated poly lactic acid bone plates using deep
fidelity of vat photopolymerization with machine learning- learning. Eng Appl Artif Intell. 2023;124:106587.
driven boundary prediction. Mater Des. 2024;241:112978. doi: 10.1016/j.engappai.2023.106587
doi: 10.1016/j.matdes.2024.112978 106. Bone JM, Childs C M, Menon A et al. Hierarchical machine
96. Chaudhary R, Fabbri P, Leoni E, Mazzanti F, learning for high-fidelity 3D printed biopolymers. ACS
Akbari R, Antonini C. Additive manufacturing by digital Biomater Sci Eng. 2020;6(12):7021-7031.
light processing: A review. Prog Addit Manuf. 2023; doi: 10.1021/acsbiomaterials.0c00755
8(2):331-351.
107. Zhang J, Wang P, Gao RX. Deep learning-based tensile
doi: 10.1007/s40964-022-00336-0 strength prediction in fused deposition modeling. Comput
97. Zakeri S, Vippola M, Levänen E. A comprehensive review Ind. 2019;107:11-21.
of the photopolymerization of ceramic resins used in doi: 10.1016/j.compind.2019.01.011
stereolithography. Addit Manuf. 2020;35:101177.
108. Li Z, Zhang Z, Shi J, Wu D. Prediction of surface roughness
doi: 10.1016/j.addma.2020.101177 in extrusion-based additive manufacturing with machine

Volume 2 Issue 2 (2025) 52 doi: 10.36922/IJAMD025130010