Page 90 - ESAM-1-1
P. 90
Engineering Science in
Additive Manufacturing ML in additive manufacturing
morphology inspection for directed energy deposition quality monitoring. J Comput Inf Sci Eng. 2024;24(10):1-17.
using small dataset with transfer learning. J Manuf Processes. doi: 10.1115/1.4066026
2023;93:101-115.
128. Cao X, Duan C, Luo X, et al. Physics-informed machine
doi: 10.1016/j.jmapro.2023.03.016 learning approach for molten pool morphology prediction
117. Safdar M, Xie J, Ko H, Lu Y, Lamouche G, Zhao YF. and process evaluation in directed energy deposition of
Transferability analysis of data-driven additive 12CrNi2 alloy steel. J Manuf Processes. 2024;119:806-826.
manufacturing knowledge: A case study between powder doi: 10.1016/j.jmapro.2024.04.023
bed fusion and directed energy deposition. Am Soc Mech
Eng. 2023:V002T02A078. 129. Zhu Q, Lu Z, Hu Y. A reality-augmented adaptive physics
informed machine learning method for efficient heat
doi: 10.1115/DETC2023-116458 transfer prediction in laser melting. J Manuf Processes.
118. Wang Z, Dai Z, Póczos B, Carbonell J. Characterizing and 2024;124:444-457.
Avoiding Negative Transfer. In: 2019 IEEE/CVF Conference doi: 10.1016/j.jmapro.2024.06.010
on Computer Vision and Pattern Recognition (CVPR); 2019.
p. 11293-11302. 130. Qin J, Taraphdar P, Sun Y, et al. Knowledge-based
bidirectional thermal variable modelling for directed
119. Safdar M, Lamouche G, Paul PP, Wood G, Zhao YF. energy deposition additive manufacturing. Virtual Phys
Engineering of Additive Manufacturing Features for Data- Prototyp. 2024;19(1):e2397008.
Driven Solutions: Sources, Techniques, Pipelines, and
Applications. Germany: Springer Nature; 2023. doi: 10.1080/17452759.2024.2397008
120. Xie J, Sage M, Zhao YF. Feature selection and feature 131. Von Rueden L, Mayer S, Beckh K, et al. Informed machine
learning in machine learning applications for gas turbines: learning-a taxonomy and survey of integrating prior
A review. Eng Appl Artif Intell. 2023;117:105591. knowledge into learning systems. IEEE Transact Knowl Data
Eng. 2021;35(1):614-633.
doi: 10.1016/j.engappai.2022.105591
doi: 10.1109/tkde.2021.3079836
121. Becker P, Roth C, Roennau A, Dillmann R. Acoustic Anomaly
Detection in Additive Manufacturing with Long Short-term 132. Zhang P, Zhou X, Ma H, et al. Anomaly detection in
Memory Neural Networks. United States: IEEE; 2020. p. 921-926. laser metal deposition with photodiode-based melt pool
monitoring system. Opt Laser Technol. 2021;144:107454.
122. Figueira A, Vaz B. Survey on synthetic data generation,
evaluation methods and GANs. Mathematics. 2022; doi: 10.1016/j.optlastec.2021.107454
10(15):2733. 133. Cherif L, Safdar M, Lamouche G, et al. Evaluation of key
spatiotemporal learners for print track anomaly classification
doi: 10.3390/math10152733
using melt pool image streams. IFAC PapersOnline.
123. Gui J, Chen T, Zhang J, et al. A survey on self-supervised 2023;56(2):4733-4739.
learning: Algorithms, applications, and future trends. IEEE
Transact Pattern Analy Mach Intell. 2024;46:9052-9071. doi: 10.48550/arXiv.2308.14861
doi: 10.1109/TPAMI.2024.3415112 134. Snow Z, Diehl B, Reutzel EW, Nassar A. Toward in-situ flaw
detection in laser powder bed fusion additive manufacturing
124. Li L, Fan Y, Tse M, Lin KY. A review of applications in through layerwise imagery and machine learning. J Manuf
federated learning. Comput Ind Eng. 2020;149:106854. Syst. 2021;59:12-26.
doi: 10.1016/j.cie.2020.106854 doi: 10.1016/j.jmsy.2021.01.008
125. Wang H, Li B, Lei L, Xuan F. Uncertainty-aware fatigue- 135. Bommasani R, Hudson DA, Adeli E, et al. On the
life prediction of additively manufactured Hastelloy X Opportunities and Risks of Foundation Models. [arXiv
superalloy using a physics-informed probabilistic neural Preprint arXiv:210807258]; 2021.
network. Reliabil Eng Syst Saf. 2024;243:109852.
136. Beltagy I, Lo K, Cohan A. SciBERT: A Pretrained
doi: 10.1016/j.ress.2023.109852 Language model for scientific Text. [arXiv Preprint arXiv:
190310676]; 2019.
126. Iyer N, Mirzendehdel AM, Raghavan S, et al. PATO:
Producibility-aware topology optimization using deep 137. Manzari ON, Ahmadabadi H, Kashiani H, Shokouhi SB,
learning for metal additive manufacturing. Int J Interact Ayatollahi A. MedViT: A robust vision transformer for
Design Manuf (IJIDeM). 2024;18:7459-7476. generalized medical image classification. Comput Biol Med.
doi: 10.1007/s12008-024-01905-z 2023;157:106791.
doi: 10.1016/j.compbiomed.2023.106791
127. Zhang S, Yang H, Yang Z, Lu Y. Engineering-guided deep
learning of melt-pool dynamics for additive manufacturing 138. Han Z, Gao C, Liu J, Zhang J, Zhang SQ. Parameter-Efficient
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