Page 67 - ESAM-1-1
P. 67
Engineering Science in
Additive Manufacturing Additive manufacturing of EH36 steels
104. Vastola G, Pei, QX Zhang YW. Predictive model for porosity doi: 10.3390/met10050683
in powder-bed fusion additive manufacturing at high beam 111. Li X, Wang P, Zhao M, Su X, Tan YH, Ding J. Customizable
energy regime. Addit Manuf. 2018;22:817-822.
anisotropic microlattices for additive manufacturing:
doi: 10.1016/j.addma.2018.05.042 Machine learning accelerated design, mechanical properties
and structural-property relationships. Addit Manuf.
105. Vastola G, Zhang G, Pei QX, Zhang YW. Controlling of
residual stress in additive manufacturing of Ti6Al4V by 2024;89:104248.
finite element modeling. Addit Manuf. 2016;12:231-239. doi: 10.1016/j.addma.2024.104248
doi: 10.1016/j.addma.2016.05.010 112. Schmitt AM, Sauer C, Höfflin D, Schiffler A. Powder
bed monitoring using semantic image segmentation to
106. Ali MH, Han YS. A finite element analysis on the effect
of scanning pattern and energy on residual stress and detect failures during 3D metal printing. Sensors (Basel).
deformation in wire arc additive manufacturing of EH36 2023;23:4183.
steel. Materials. 2023;16:4698. doi: 10.3390/s23094183
doi: 10.3390/ma16134698 113. Herzog T, Brandt M, Trinchi A, Sola A, Hagenlocher C,
Molotnikov A. Defect detection by multi-axis infrared
107. Dharmadhikari S, Menon N, Basak A. A reinforcement
learning approach for process parameter optimization in process monitoring of laser beam directed energy deposition.
additive manufacturing. Addit Manuf. 2023;71:103556. Sci Rep. 2024;14:3861.
doi: 10.1038/s41598-024-53931-2
doi: 10.1016/j.addma.2023.103556
114. Chen L, Yao X, Xu P, Moon SK, Bi G. Rapid surface defect
108. Nguyen DS, Park HS, Lee CM. Optimization of selective
laser melting process parameters for Ti-6Al-4V alloy identification for additive manufacturing with in-situ
manufacturing using deep learning. J Manuf Process. point cloud processing and machine learning. Virtual Phys
2020;55:230-235. Prototyp. 2021;16:50-67.
doi: 10.1080/17452759.2020.1832695
doi: 10.1016/j.jmapro.2020.04.014
115. Ng WL, Goh GL, Goh GD, Ten JSJ, Yeong WY. Progress
109. Karkaria V, Goeckner A, Zha R, et al. Towards a digital twin
framework in additive manufacturing: Machine learning and and opportunities for machine learning in materials
bayesian optimization for time series process optimization. and processes of additive manufacturing. Adv Mater.
J Manuf Syst. 2024;75:322-332. 2024;36:2310006.
doi: 10.1002/adma.202310006
doi: 10.1016/j.jmsy.2024.04.023
116. Jin Z, Zhang Z, Demir K, Gu GX. Machine learning for
110. Mondal S, Gwynn D, Ray A, Basak A. Investigation of melt
pool geometry control in additive manufacturing using advanced additive manufacturing. Matter. 2020;3:1541-1556.
hybrid modeling. Metals. 2020;10:683. doi: 10.1016/j.matt.2020.08.023
Volume 1 Issue 1 (2025) 18 doi: 10.36922/ESAM025060005

