Page 31 - ESAM-1-1
P. 31
Engineering Science in
Additive Manufacturing ML in MAM monitoring and control through images
integrating CNN and GRU to predict particulate matter 114. Lu Y, Wang Y. Machine fault diagnosis of fused filament
concentrations. Process Saf Environ Protect. 2023;173:604-613. fabrication process with physics-constrained dictionary
learning. Proc Manuf. 2021;53:726-734.
doi: 10.1016/j.psep.2023.03.052
doi: 10.1016/j.promfg.2021.06.071
104. Guo S, Agarwal M, Cooper C, et al. Machine learning for
metal additive manufacturing: Towards a physics-informed 115. Lu Y, Wang Y. Active physics-constrained dictionary
data-driven paradigm. J Manuf Syst. 2022;62:145-163. learning to diagnose nozzle conditions in fused filament
fabrication process. Manuf Lett. 2023;35:973-982.
doi: 10.1016/j.jmsy.2021.11.003
105. Zhou T, Li Q, Lu H, Cheng Q, Zhang X. GAN review: doi: 10.1016/j.mfglet.2023.08.043
Models and medical image fusion applications. Inform 116. Lin X, Shen AC, Ni DW, Fuh JYH, Zhu KP. In situ defect
Fusion. 2023;91:134-148. detection in selective laser melting using a multi-feature
doi: 10.1016/j.inffus.2022.10.017 fusion method. IFAC PapersOnLine. 2023;56:4725-4732.
106. Deng W, Liu H, Xu J, Zhao H, Song Y. An improved quantum- doi: 10.1016/j.ifacol.2023.10.1234
inspired differential evolution algorithm for deep belief 117. Xie J, Jiang TY, Chen X. An Image Segmentation Framework
network. IEEE Trans Instrum Measur. 2020;69(10):7319-7327. for in-Situ Monitoring in Laser Powder Bed Fusion Additive
doi: 10.1109/tim.2020.2983233 Manufacturing. In: IFAC-PapersOnLine Proceedings of
2 Modeling, Estimation and Control Conference (MECC);
nd
107. Wang S, Liu H, Gomes PH, Krishnamachari B. Deep 2022. p. 800-806.
reinforcement learning for dynamic multichannel access
in wireless networks. IEEE Trans Cogn Commun Netw. doi: 10.1016/j.ifacol.2022.11.280
2018;4(2):257-265. 118. Feng S, Chen ZE, Bircher B, Ji Z, Nyborg L, Bigot S.
doi: 10.1109/tccn.2018.2809722 Predicting laser powder bed fusion defects through
in-process monitoring data and machine learning. Mater
108. Zhu Q, Liu Z, Yan J. Machine learning for metal additive Des. 2022;222:111115.
manufacturing: Predicting temperature and melt pool
fluid dynamics using physics-informed neural networks. doi: 10.1016/j.matdes.2022.111115
Computat Mechan. 2021;67(2):619-635. 119. Li JC, Zhou Q, Huang XF, Li ML, Cao LC. In situ quality
doi: 10.1007/s00466-020-01952-9 inspection with layer-wise visual images based on deep
transfer learning during selective laser melting. J Intell
109. Zhu T, Zheng Q, Lu Y. Physics-informed fully convolutional Manuf. 2023;34(2):853-867.
networks for forward prediction of temperature field and
inverse estimation of thermal diffusivity. J Comput Inform doi: 10.1007/s10845-021-01829-5
Sci Eng. 2024;24(11):111004. 120. Kaji F, Nguyen-Huu H, Budhwani A, Narayanan JA,
doi: 10.1115/1.4064555 Zimny M, Toyserkani E. A deep-learning-based in-situ
surface anomaly detection methodology for laser directed
110. Lu Y, Wang Y. Monitoring temperature in additive energy deposition via powder feeding. J Manuf Process.
manufacturing with physics-based compressive sensing. 2022;81:624-637.
J Manuf Syst. 2018;48:60-70.
doi: 10.1016/j.jmapro.2022.06.046
doi: 10.1016/j.jmsy.2018.05.010
121. Akbari P, Ogoke F, Kao NY, et al. MeltpoolNet: Melt pool
111. Lu Y, Shevtshenko E, Wang Y. Physics-based compressive characteristic prediction in metal additive manufacturing
sensing to enable digital twins of additive manufacturing using machine learning. Addit Manuf. 2022;55:102817.
processes. J Comput Inform Sci Eng. 2021;21(3):031009.
doi: 10.1016/j.addma.2022.102817
doi: 10.1115/1.4050377
122. Anand N, Chang KC, Yeh AC, Chen YB, Lee MT.
112. Lu Y, Wang Y. An efficient transient temperature monitoring Development of a comprehensive model for predicting
of fused filament fabrication process with physics-based melt pool characteristics with dissimilar materials in
compressive sensing. IISE Trans. 2019;51(2):168-180.
selective laser melting processes. J Mater Process Technol.
doi: 10.1080/24725854.2018.1499054 2023;319:118069.
113. Lu Y, Wang Y, Pan L. A feature-based physics-constrained doi: 10.1016/j.jmatprotec.2023.118069
active dictionary learning scheme for image-based additive 123. Kan WH, Chiu LNS, Lim CVS, et al. A critical review on
manufacturing process monitoring. J Manuf Process. the effects of process-induced porosity on the mechanical
2023;103:261-273. properties of alloys fabricated by laser powder bed fusion.
doi: 10.1016/j.jmapro.2023.08.040 J Mater Sci. 2022;57(21):9818-9865.
Volume 1 Issue 1 (2025) 25 doi: 10.36922/esam.8548

