Page 52 - ESAM-1-4

Engineering Science in
Additive Manufacturing Machine learning for biomedical metal AM

doi: 10.3390/met10010103 analysis and machine learning. Int J Adv Manuf Technol.
2024;132(9):5087-5101.
129. Liu L, Ju F, Kim S. Online thermal profile prediction for
large format additive manufacturing: A hybrid CNN-LSTM doi: 10.1007/s00170-024-13641-5
based approach. Addit Manuf. 2025;109:104882.
140. Montazeri M, Nassar AR, Dunbar AJ, Rao P. In-process
doi: 10.1016/j.addma.2025.104882 monitoring of porosity in additive manufacturing using
130. Lopez A, Bacelar R, Pires I, Santos TG, Sousa JP, Quintino L. optical emission spectroscopy. IISE Trans. 2020;52(5):500-515.
Non-destructive testing application of radiography and doi: 10.1080/24725854.2019.1659525
ultrasound for wire and arc additive manufacturing. Addit
Manuf. 2018;21:298-306. 141. Chen X, Fu Y, Kong F, et al. An in-process multi-feature data
fusion nondestructive testing approach for wire arc additive
doi: 10.1016/j.addma.2018.03.020 manufacturing. Rapid Prototyp J. 2021;28(3):573-584.
131. Wang J, Zhang X, Lu Y. Machine learning in image-based doi: 10.1108/rpj-02-2021-0034
metal additive manufacturing process monitoring and
control: A review. Eng Sci Addit Manuf. 2025;1(1):8548. 142. Gaikwad A, Giera B, Guss GM, Forien JB, Matthews MJ,
Rao P. Heterogeneous sensing and scientific machine
doi: 10.36922/esam.8548 learning for quality assurance in laser powder bed fusion - A
132. Ansari MA, Crampton A, Garrard R, Cai B, Attallah M. single-track study. Addit Manuf. 2020;36:101659.
A convolutional neural network (CNN) classification to doi: 10.1016/j.addma.2020.101659
identify the presence of pores in powder bed fusion images.
Int J Adv Manuf Technol. 2022;120(7):5133-5150. 143. Knaak C, Masseling L, Duong E, Abels P, Gillner A. Improving
build quality in laser powder bed fusion using high dynamic
doi: 10.1007/s00170-022-08995-7 range imaging and model-based reinforcement learning.
133. Lee H, Heogh W, Yang J, et al. Deep learning for in-situ IEEE Access. 2021;9:55214-55231.
powder stream fault detection in directed energy deposition doi: 10.1109/ACCESS.2021.3067302
process. J Manuf Syst. 2022;62:575-587.
144. Scime L, Beuth J. A multi-scale convolutional neural network
doi: 10.1016/j.jmsy.2022.01.013 for autonomous anomaly detection and classification in a
134. Yang Z, Zhu L, Dun Y, et al. In-situ monitoring of laser powder bed fusion additive manufacturing process.
the melt pool dynamics in ultrasound-assisted metal Addit Manuf. 2018;24:273-286.
3D printing using machine learning. Virtual Phys doi: 10.1016/j.addma.2018.09.034
Prototyp. 2023;18(1):e2251453.
145. Chen L, Bi G, Yao X, et al. Multisensor fusion-based digital
doi: 10.1080/17452759.2023.2251453 twin for localized quality prediction in robotic laser-
135. Mi J, Zhang Y, Li H, et al. In-situ monitoring laser based directed energy deposition. Robot Comput Integr Manuf.
directed energy deposition process with deep convolutional 2023;84:102581.
neural network. J Intell Manuf. 2023;34(2):683-693. doi: 10.1016/j.rcim.2023.102581
doi: 10.1007/s10845-021-01820-0 146. Rescsanski S, Hebert R, Haghighi A, Tang J, Imani F. Towards
136. Prem PR, Sanker AP, Sebastian S, Kaliyavaradhan SK. intelligent cooperative robotics in additive manufacturing:
A review on application of acoustic emission testing during Past, present, and future. Robot Comput Integr Manuf.
additive manufacturing. J Nondestr Eval. 2023;42(4):96. 2025;93:102925.
doi: 10.1007/s10921-023-01005-0 doi: 10.1016/j.rcim.2024.102925
137. Yu Q, Zhang M, Mujumdar AS, Li J. AI-based additive 147. Abranovic B, Sarkar S, Chang-Davidson E, Beuth J. Melt
manufacturing for future food: Potential applications, pool level flaw detection in laser hot wire directed energy
challenges and possible solutions. Innov Food Sci Emerg deposition using a convolutional long short-term memory
Technol. 2024;92:103599. autoencoder. Addit Manuf. 2024;79:103843.
doi: 10.1016/j.ifset.2024.103599 doi: 10.1016/j.addma.2023.103843
138. Luo S, Ma X, Xu J, Li M, Cao L. Deep learning based 148. Reutzel EW, Nassar AR. A survey of sensing and control
monitoring of spatter behavior by the acoustic signal in systems for machine and process monitoring of directed-
selective laser melting. Sensors (Basel). 2021;21(21):7179. energy, metal-based additive manufacturing. Rapid Prototyp
J. 2015;21(2):159-167.
doi: 10.3390/s21217179
doi: 10.1108/rpj-12-2014-0177
139. Rahman MA, Jamal S, Cruz MV, Silwal B, Taheri H. In situ
process monitoring of multi-layer deposition in wire arc 149. Wang Q, Michaleris P, Nassar AR, Irwin JE, Ren Y,
additive manufacturing (WAAM) process with acoustic data Stutzman CB. Model-based feedforward control of laser

Volume 1 Issue 4 (2025) 30 doi: 10.36922/ESAM025440031