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



            83.  Li W, Lambert-Garcia R, Getley ACM,  et al. AM-SegNet      doi: 10.1089/3dp.2023.0186
               for additive manufacturing  in situ X-ray image   93.  Kononenko DY, Nikonova V, Seleznev M, van den Brink J,
               segmentation and feature quantification.  Virtual Phys   Chernyavsky D. An  in situ crack detection approach in
               Prototyp. 2024;19(1):e2325572.
                                                                  additive manufacturing based on acoustic emission and
               doi: 10.1080/17452759.2024.2325572                 machine learning. Addit Manuf Lett. 2023;5:100130.
            84.  Rees DT, Leung CLA, Elambasseril J,  et al.  In situ X-ray      doi: 10.1016/j.addlet.2023.100130
               imaging of hot cracking and porosity during LPBF of   94.  Chen Y, Jiang L, Peng Y,  et al. Ultra-fast laser ultrasonic
               Al-2139 with TiB2 additions and varied process parameters.   imaging method for online inspection of metal additive
               Mater Des. 2023;231:112031.                        manufacturing. Optics Lasers Eng. 2023;160:107244.
               doi: 10.1016/j.matdes.2023.112031                  doi: 10.1016/j.optlaseng.2022.107244
            85.  Pandiyan V, Masinelli G, Claire N, et al. Deep learning-based   95.  Rahman MA, Jamal S, Cruz MV, Silwal B, Taheri H. In situ
               monitoring of laser powder bed fusion process on variable   process monitoring of multi-layer deposition in wire arc
               time-scales using heterogeneous sensing and operando   additive manufacturing (WAAM) process with acoustic data
               X-ray radiography guidance. Addit Manuf. 2022;58:103007.  analysis  and  machine  learning.  Int J Adv Manuf Technol.
               doi: 10.1016/j.addma.2022.103007                   2024;132(9-10):5087-5101.
            86.  Li J, Hughes AE,  Yang YS,  et al. Quantitative 3D      doi: 10.1007/s00170-024-13641-5
               characterization for kinetics of corrosion initiation and   96.  Chen L, Bi G, Yao X, et al. Multisensor fusion-based digital
               propagation in additively manufactured austenitic stainless   twin for localized quality prediction in robotic laser-
               steel. Adv Sci. 2022;9(36):e202201162.             directed energy deposition.  Robot Comput Integr Manuf.
               doi: 10.1002/advs.202201162                        2023;84:102581.
            87.  Wu Y, Zhang D, Hou H,  et al.  In situ synchrotron X-ray      doi: 10.1016/j.rcim.2023.102581
               diffraction study: Phase evolution in transition zone of   97.  Li K, Li T, Ma M, Wang D, Deng W, Lu H. Laser cladding
               TiAl/Ti2AlNb dual alloy fabricated by laser-directed energy   state recognition and crack defect diagnosis by acoustic
               deposition. Scripta Mater. 2025;255:116340.        emission signal and neural network. Optics Laser Technol.
               doi: 10.1016/j.scriptamat.2024.116340              2021;142:107161.
            88.  Wang H, Gould B, Haddad M, Wu Z, Wolff SJ.  In  situ      doi: 10.1016/j.optlastec.2021.107161
               X-ray imaging of directed energy deposition of metals: The   98.  Imani F, Chen R, Diewald E, Reutzel E, Yang H. Deep
               comparisons of delivery performance between spherical and   learning of variant geometry in layerwise imaging profiles
               irregular powders. J Manuf Process. 2022;79:11-18.  for additive manufacturing quality control. J Manuf Sci Eng.
               doi: 10.1016/j.jmapro.2022.04.037                  2019;141(11):11101.
            89.  Ioannidou C, Konig HH, Semjatov N, et al.  In-situ      doi: 10.1115/1.4044420
               synchrotron X-ray analysis of metal additive manufacturing:   99.  Gobert C, Reutzel EW, Petrich J, Nassar AR, Phoha S.
               Current state, opportunities and challenges.  Mater  Des.   Application of  supervised machine learning  for  defect
               2022;219:110790.                                   detection during metallic powder bed fusion additive
               doi: 10.1016/j.matdes.2022.110790                  manufacturing using high resolution imaging. Addit Manuf.
                                                                  2018;21:517-528.
            90.  Sun WB, Zhang ZH, Ren WJ, Mazumder J, Jin JH. In situ
               monitoring of optical emission spectra for microscopic      doi: 10.1016/j.addma.2018.04.005
               pores in metal additive manufacturing.  J  Manuf Sci Eng   100.  Niu ZY, Zhong GQ, Yu H. A review on the attention mechanism
               Trans ASME. 2022;144(1):011006.                    of deep learning. Neurocomputing. 2021;452:48-62.
               doi: 10.1115/1.4051532                             doi: 10.1016/j.neucom.2021.03.091
            91.  Jayasinghe S, Paoletti P, Jones N, Green PL. Predicting gas   101. Shevchik S, Le-Quang T, Meylan B, et al. Supervised deep
               pores from photodiode measurements in laser powder bed   learning for real-time quality monitoring of laser welding
               fusion builds. Prog Addit Manuf. 2024;9(4):885-888.  with X-ray radiographic guidance. Sci Rep. 2020;10(1)3389.
               doi: 10.1007/s40964-023-00489-6                    doi: 10.1038/s41598-020-60294-x
            92.  Luo QX, Shimanek JD, Simpson TW, Beese AM. An   102. Zhang S, Li X, Zong M, Zhu X, Cheng D. Learning k for kNN
               image-based transfer learning approach for using  in situ   classification. ACM Trans Intell Syst Technol. 2017;8(3):43.
               processing data to predict laser powder bed fusion additively
               manufactured Ti-6Al-4V mechanical properties.  3D Print      doi: 10.1145/2990508
               Addit Manuf. 2024;12.                           103.  Guo Z, Yang C, Wang D, Liu H. A novel deep learning model


            Volume 1 Issue 1 (2025)                         24                             doi: 10.36922/esam.8548
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