Page 89 - ESAM-1-1

Engineering Science in
Additive Manufacturing ML in additive manufacturing

manufacturing. Addit Manuf. 2021;48:102411. learning approach to heat emission prediction in laser
powder bed fusion. J Manuf Sci Eng. 2024;146(10):1-48.
doi: 10.1016/j.addma.2021.102411
doi: 10.1115/1.4065888
95. Wong VWH, Ferguson M, Law KH, Lee YTT, Witherell P.
Segmentation of additive manufacturing defects using 106. Li Y, Mojumder S, Lu Y, et al. Statistical parameterized
U-Net. J Comput Inf Sci Eng. 2022;22(3):1-44. physics-based machine learning digital shadow models
for laser powder bed fusion process. Addit Manuf.
doi: 10.1115/1.4053078
2024;87:104214.
96. Kim Y, Lee T, Hyun Y, et al. Self-supervised representation doi: 10.1016/j.addma.2024.104214
learning anomaly detection methodology based on
boosting algorithms enhanced by data augmentation using 107. Uhrich B, Pfeifer N, Schäfer M, Theile O, Rahm E. Physics-
StyleGAN for manufacturing imbalanced data. Comput Ind. informed deep learning to quantify anomalies for real-time fault
2023;153:104024. mitigation in 3D printing. Appl Intell. 2024;54(6):4736-4755.
doi: 10.1016/j.compind.2023.104024 doi: 10.1007/s10489-024-05402-4
97. Zhu Z, Ferreira K, Anwer N, Mathieu L, Guo K, 108. Shi T, Ma M, Wu J, Post C, Charles E, Schmitz T. AFSD-Physics:
Qiao L. Convolutional neural network for geometric Exploring the Governing Equations of Temperature Evolution
deviation prediction in additive manufacturing. Proced During Additive Friction Stir deposition By a Human-AI
Cirp. 2020;91:534-539. Teaming Approach. [arXiv Preprint arXiv:240116501]; 2024.
98. Tian Q, Guo S, Melder E, Bian L, Guo WG. Deep learning- 109. Peng B, Panesar A. Multi-layer thermal simulation
based data fusion method for in situ porosity detection using physics-informed neural network. Addit Manuf.
in laser-based additive manufacturing. J Manuf Sci Eng. 2024;95:104498.
2021;143(4):041011. doi: 10.1016/j.addma.2024.104498
doi: 10.1115/1.4048957 110. Kim J, Yang Z, Lu Y. A physics-guided deep generative
99. Houser E, Shashaani S, Harrysson O, Jeon Y. Predicting model for predicting melt pool behavior in laser powder bed
additive manufacturing defects with robust feature selection fusion additive manufacturing. J Intell Manuf. 2024:1-21.
for imbalanced data. IISE Transact. 2024;56(9):1001-1019. doi: 10.1007/s10845-024-02504-1
doi: 10.1080/24725854.2023.2207633 111. Sajadi P, Rahmani Dehaghani M, Tang Y, Wang GG. Physics-
100. Li Y, Shi Z, Liu C, Tian W, Kong Z, Williams CB. Augmented informed online learning for temperature prediction in
time regularized generative adversarial network (atr-gan) metal AM. Materials (Basel). 2024;17(13):3306.
for data augmentation in online process anomaly detection. doi: 10.3390/ma17133306
IEEE Transact Autom Sci Eng. 2021;19(4):3338-3355.
112. Whitney BC, Spangenberger AG, Rodgers TM, Lados DA.
doi: 10.1109/TASE.2021.3118635 Part-scale microstructure prediction for laser powder bed
101. Fan SKS, Tsai DM, Yeh PC. Effective variational- fusion Ti-6Al-4V using a hybrid mechanistic and machine
autoencoder-based generative models for highly imbalanced learning model. Addit Manuf. 2024;94:104500.
fault detection data in semiconductor manufacturing. IEEE doi: 10.1016/j.addma.2024.104500
Transact on Semicond Manuf. 2023;36(2):205-214.
113. Chen L, Yang F, Wang R, Zhang Y, Diao Z, Rong M. Optical
doi: 10.1109/TSM.2023.3238555 spectral physics-informed attention network for condition
102. Yangue E, Fullington D, Smith O, Tian W, Liu C. Diffusion monitoring in WAAM. IEEE Transact Ind Electron.
generative model-based learning for smart layer-wise 2023;71:9708-9718.
monitoring of additive manufacturing. J Comput Inf Sci Eng. doi: 10.1109/TIE.2023.3325570
2024;24(6):060903.
114. Yu H, Hu Y, Kang G, Peng X, Chen B, Wu S. High-cycle
doi: 10.1115/1.4065092 fatigue life prediction of L-PBF AlSi10Mg alloys: A domain
103. Zheng F, Xie L, Bai Q, et al. Semi-supervised learning for knowledge-guided symbolic regression approach. Philos
laser directed energy deposition monitoring via co-axial Trans A Math Phys Eng Sci. 2024;382(2264):20220383.
dynamic imaging. Addit Manuf. 2025;97:104628. doi: 10.1098/rsta.2022.0383
104. Shi N, Kontar RA. Personalized federated learning via 115. Li J, Zhou Q, Huang X, Li M, Cao L. In situ quality inspection
domain adaptation with an application to distributed 3D with layer-wise visual images based on deep transfer learning
printing. Technometrics. 2023;65(3):328-339. during selective laser melting. J Intell Manuf. 2023;34:853-867.
doi: 10.1080/00401706.2022.2157882 doi: 10.1007/s10845-021-01829-5
105. Lei R, Guo Y, Yan J, Guo W. Bridging data gaps: A federated 116. Zhu X, Jiang F, Guo C, Xu D, Wang Z, Jiang G. Surface

Volume 1 Issue 1 (2025) 22 doi: 10.36922/ESAM025040004