Page 32 - ESAM-1-1

Engineering Science in
Additive Manufacturing ML in MAM monitoring and control through images

doi: 10.1007/s10853-022-06990-7 doi: 10.1016/j.eswa.2024.124150
124. Cooke S, Ahmadi K, Willerth S, Herring R. Metal additive 135. Terry BS, Baucher B, Chaudhary AB, Chakraborty S. Active
manufacturing: Technology, metallurgy and modelling. monitoring of powder bed fusion process by training an
J Manuf Process. 2020;57:978-1003. artificial neural net classifier on layer-by-layer surface laser
profilometry data. Int J Adv Manuf Technol. 2022;119(11-
doi: 10.1016/j.jmapro.2020.07.025
12):7765-7786.
125. Wu J, Huang C, Li ZL, Li RS, Wang GL, Zhang HO. An in
situ surface defect detection method based on improved doi: 10.1007/s00170-022-08722-2
you only look once algorithm for wire and arc additive 136. Jiang ZM, Zhang AM, Chen ZD, et al. A deep convolutional
manufacturing. Rapid Prototyp J. 2023;29(5):910-920. network combining layerwise images and defect parameter
doi: 10.1108/rpj-06-2022-0211 vectors for laser powder bed fusion process anomalies
classification. J Intell Manuf. 2024;35(6):2929-2959.
126. Jamnikar ND, Liu S, Brice C, Zhang XL. In-process
comprehensive prediction of bead geometry for laser wire- doi: 10.1007/s10845-023-02183-4
feed DED system using molten pool sensing data and 137. Ogoke F, Pak P, Myers A, et al. Deep learning for melt pool
multi-modality CNN. Int J Adv Manuf Technol. 2022;121(1- depth contour prediction from surface thermal images via
2):903-917. vision transformers. Addit Manuf Lett. 2024;11:100243.
doi: 10.1007/s00170-022-09248-3 doi: 10.1016/j.addlet.2024.100243
127. Lu Y, Wang Y. Physics-constrained dictionary learning for 138. Snow Z, Reutzel EW, Petrich J. Correlating in-situ sensor
selective laser melting process monitoring. In: Proceedings data to defect locations and part quality for additively
of the 2021 IISE Annual Conference; May 2021. p. 265-270. manufactured parts using machine learning. J Mater Process
128. Zhu YH, Wu XF, Gotawala N, Higdon DM, Yu HZ. Thermal Technol. 2022;302:117476.
prediction of additive friction stir deposition through doi: 10.1016/j.jmatprotec.2021.117476
Bayesian learning- enabled explainable artificial intelligence.
J Manuf Syst. 2024;72:1-15. 139. Cho HW, Shin SJ, Seo GJ, Kim DB, Lee DH. Real-time
anomaly detection using convolutional neural network in
doi: 10.1016/j.jmsy.2023.10.015 wire arc additive manufacturing: Molybdenum material.
129. Lu Y, Wang Y. Physics based compressive sensing to monitor J Mater Process Technol. 2022;302:117495.
temperature and melt flow in laser powder bed fusion. Addit doi: 10.1016/j.jmatprotec.2022.117495
Manuf. 2021;47:102304.
140. Chen R, Sodhi M, Imani M, Khanzadeh M, Yadollahi A,
doi: 10.1016/j.addma.2021.102304 Imani F. Brain-inspired computing for in-process melt pool
130. Repossini G, Laguzza V, Grasso M, Colosimo BM. On the characterization in additive manufacturing. Cirp J Manuf Sci
use of spatter signature for in-situ monitoring of Laser Technol. 2023;41:380-390.
Powder Bed Fusion. Addit Manuf. 2017;16:35-48. doi: 10.1016/j.cirpj.2022.12.009
doi: 10.1016/j.addma.2017.05.004 141. Vallabh CKP, Sridar S, Xiong W, Zhao X. Predicting melt
131. Yin J, Wang D, Yang L, et al. Correlation between forming pool depth and grain length using multiple signatures from
quality and spatter dynamics in laser powder bed fusion. in-situ single camera two-wavelength imaging pyrometry
Addit Manuf. 2020;31:100958. for laser powder bed fusion. J Mater Process Technol.
2022;308:117724.
doi: 10.1016/j.addma.2019.100958
doi: 10.1016/j.jmatprotec.2022.117724
132. Ye D, Hsi Fuh JY, Zhang Y, Hong GS, Zhu K. In situ monitoring
of selective laser melting using plume and spatter signatures 142. Zhang HL, Vallabh CKP, Zhao XY. Influence of spattering on
by deep belief networks. ISA Trans. 2018;81:96-104. in-process layer surface roughness during laser powder bed
fusion. J Manuf Process. 2023;104:289-306.
doi: 10.1016/j.isatra.2018.07.021
doi: 10.1016/j.jmapro.2023.08.058
133. Li JC, Zhou Q, Cao LC, Wang YZ, Hu JX. A convolutional
neural network-based multi-sensor fusion approach for 143. Hespeler S, Dehghan-Niri E, Juhasz M, Luo K, Halliday HS.
in-situ quality monitoring of selective laser melting. J Manuf Deep learning for in-situ layer quality monitoring during
Syst. 2022;64:429-442. laser-based directed energy deposition (LB-DED) additive
manufacturing process. Appl Sci Basel. 2022;12(18):8974.
doi: 10.1016/j.jmsy.2022.07.007
doi: 10.3390/app12188974
134. Li JC, Hu JX, Zhou Q, Zhang YH. Transfer learning-based
quality monitoring of laser powder bed fusion across 144. Shin H, Lee J, Choi SK, Lee SW. Development of multi-defect
materials. Expert Syst Appl. 2024;252:124150. diagnosis algorithm for the directed energy deposition

Volume 1 Issue 1 (2025) 26 doi: 10.36922/esam.8548