Page 60 - IJAMD-2-2
P. 60
International Journal of AI for
Materials and Design ML-driven optimization in additive manufacturing
challenges in additive manufacturing of functionally doi: 10.1007/s10845-024-02355-w
graded metallic materials via powder-fed laser directed 143. Guirguis D, Tucker C, Beuth J. Accelerating process
energy deposition: A review. J Mater Process Technol. development for 3D printing of new metal alloys. Nat
2021;294:117117.
Commun. 2024;15(1):582.
doi: 10.1016/j.jmatprotec.2021.117117
doi: 10.1038/s41467-024-44783-5
133. Kim H, Seo J, Chung Baek AM, et al. Direct energy 144. Lee H, Heogh W, Yang J, et al. Deep learning for in-situ
deposition for smart micro reactor. Virtual Phys
Prototyp. 2024;19(1):e2411024. powder stream fault detection in directed energy deposition
process. J Manuf Syst. 2022;62:575-587.
doi: 10.1080/17452759.2024.2411024
doi: 10.1016/j.jmsy.2022.01.013
134. Brennan MC, Keist JS, Palmer TA. Defects in metal
additive manufacturing processes. J Mater Eng Perform. 145. Karkaria V, Goeckner A, Zha R, et al. Towards a digital twin
framework in additive manufacturing: Machine learning and
2021;30:4808-4818.
bayesian optimization for time series process optimization.
doi: 10.1007/s11665-021-05919-6 J Manuf Syst. 2024;75:322-332.
135. Du Plessis A, Yadroitsava I, Yadroitsev I. Effects of defects doi: 10.1016/j.jmsy.2024.04.023
on mechanical properties in metal additive manufacturing:
A review focusing on X-ray tomography insights. Mater Des. 146. Tan XP, Tan YJ, Chow CSL, Tor SB, Yeong WY. Metallic
2020;187:108385. powder-bed based 3D printing of cellular scaffolds
for orthopaedic implants: A state-of-the-art review on
doi: 10.1016/j.matdes.2019.108385 manufacturing, topological design, mechanical properties
136. Jiang M, Mukherjee T, Du Y, DebRoy T. Superior printed and biocompatibility. Mater Sci Eng C. 2017;76:1328-1343.
parts using history and augmented machine learning. NPJ doi: 10.1016/j.msec.2017.02.094
Comput Mater. 2022;8(1):184.
147. Ladd C, So JH, Muth J, Dickey MD. 3D printing of
doi: 10.1038/s41524-022-00866-9 free standing liquid metal microstructures. Adv Mater.
137. Zhang Y, Lin C, Tian Y, et al. Machine learning enhanced 2013;25(36):5081-5085.
metal 3D printing: High throughput optimization and doi: 10.1002/adma.201301400
material transfer extensibility. Int J Extrem Manuf.
2025;7:045004. 148. Huang DJ, Li H. A machine learning guided investigation
of quality repeatability in metal laser powder bed fusion
doi: 10.1088/2631-7990/adbb96 additive manufacturing. Mater Des. 2021;203:109606.
138. Ogoke F, Farimani AB. Thermal control of laser powder bed doi: 10.1016/j.matdes.2021.109606
fusion using deep reinforcement learning. Addit Manuf.
2021;46:102033. 149. Montalbano T, Nimer S, Daffron M, Croom B, Ghosh S,
Storck S. Machine learning enabled discovery of new
doi: 10.1016/j.addma.2021.102033 L-PBF processing domains for Ti-6Al-4V. Addit Manuf.
139. Zhong Q, Tian X, Huang X, Huo C, Li D. Using feedback 2025;98:104632.
control of thermal history to improve quality consistency doi: 10.1016/j.addma.2024.104632
of parts fabricated via large-scale powder bed fusion. Addit
Manuf. 2021;42:101986. 150. Asadi R, Queguineur A, Wiikinkoski O, et al. Process
monitoring by deep neural networks in directed energy
doi: 10.1016/j.addma.2021.101986 deposition: CNN-based detection, segmentation, and
140. Zhang Z, Liu Z, Wu D. Prediction of melt pool temperature statistical analysis of melt pools. Robot Comput Integr
in directed energy deposition using machine learning. Addit Manuf. 2024;87:102710.
Manuf. 2021;37:101692. doi: 10.1016/j.rcim.2023.102710
doi: 10.1016/j.addma.2020.101692 151. Kim T, Kim JG, Park S, et al. Virtual surface morphology
141. Abranovic B, Sarkar S, Chang-Davidson E, Beuth J. Melt generation of Ti-6Al-4V directed energy deposition via
pool level flaw detection in laser hot wire directed energy conditional generative adversarial network. Virtual Phys
deposition using a convolutional long short-term memory Prototyp. 2023;18(1):e2124921.
autoencoder. Addit Manuf. 2024;79:103843.
doi: 10.1080/17452759.2022.2124921
doi: 10.1016/j.addma.2023.103843
152. Yang Z, Zhu L, Dun Y, et al. In-situ monitoring of
142. Williams RJ, Sing SL. Spatiotemporal analysis of powder bed the melt pool dynamics in ultrasound-assisted metal
fusion melt pool monitoring videos using deep learning. 3D printing using machine learning. Virtual Phys
J Intell Manuf. 2024;36:2409-2422. Prototyp. 2023;18(1):e2251453.
Volume 2 Issue 2 (2025) 54 doi: 10.36922/IJAMD025130010

