Page 28 - ESAM-1-1
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Engineering Science in
            Additive Manufacturing                                        ML in MAM monitoring and control through images



               imaging. Addit Manuf. 2018;24:647-657.             doi: 10.1007/s10845-022-01977-2
               doi: 10.1016/j.addma.2018.08.025                53.  Kim S, Jeon I, Sohn H. Infrared thermographic imaging
                                                                  based real-time layer height estimation during directed
            44.  Esmaeilzadeh R, Pandiyan V, Van Petegem S, et al. Acoustic
               emission signature of martensitic transformation in laser   energy deposition. Optics Lasers Eng. 2023;168:107661.
               powder bed fusion of Ti6Al4V-Fe, supported by operando      doi: 10.1016/j.optlaseng.2023.107661
               X-ray diffraction. Addit Manuf. 2024;96:104562.
                                                               54.  Marshall GJ, Thompson SM, Shamsaei N. Data indicating
               doi: 10.1016/j.addma.2024.104562                   temperature response of Ti–6Al–4V thin-walled structure
                                                                  during its additive manufacture via Laser Engineered Net
            45.  Ansari MJ, Arcondoulis EJG, Roccisano A, Schulz C,
               Schlaefer T, Hall C. Optimized analytical approach for   Shaping. Data Brief. 2016;7:697-703.
               the detection of process-induced defects using acoustic      doi: 10.1016/j.dib.2016.02.084
               emission during directed energy deposition process. Addit   55.  Zheng L, Zhang Q, Cao H,  et al. Melt pool boundary
               Manuf. 2024;86:104218.
                                                                  extraction and its width prediction from infrared images in
               doi: 10.1016/j.addma.2024.104218                   selective laser melting. Mater Des. 2019;183:108110.
            46.  Li JC, Cao LC, Xu J, Wang SY, Zhou Q.  In situ porosity      doi: 10.1016/j.matdes.2019.108110
               intelligent classification of selective laser melting based on   56.  Yan DQ, Pasebani S, Fan ZY. Correlation study between the
               coaxial monitoring and image processing.  Measurement.   in-Situ Thermal Signatures and Surface Roughness Produced
               2022;187:110232.
                                                                  by Laser Powder Bed Fusion. In: Presented at: Proceedings
               doi: 10.1016/j.measurement.2021.110232             of ASME 2024 19  International Manufacturing Science and
                                                                               th
                                                                  Engineering Conference, MSEC2024. Vol. 1; 2024.
            47.  Cannizzaro  D,  Varrella  AG,  Paradisot  S,  et al.  Image
               analytics and machine learning for in-situ defects detection      doi: 10.1115/MSEC2024-124781
               in Additive Manufacturing. In:  Conference: 2021 Design,
               Automation & Test in Europe Conference & Exhibition; 2021.   57.  Krauss H, Zeugner T, Zaeh MF. Layerwise monitoring of the
               p. 603-608.                                        selective laser melting process by thermography. Phys Proc.
                                                                  2014;56:64-71.
               doi: 10.23919/DATE51398.2021.9474175
                                                                  doi: 10.1016/j.phpro.2014.08.097
            48.  Iravani-Tabrizipour M, Toyserkani E. An image-based   58.  Gould B, Wolff S, Parab N,  et al.  In situ analysis of laser
               feature tracking algorithm for real-time measurement of   powder bed fusion using simultaneous high-speed infrared
               clad height. Machine Vision Appl. 2007;18(6):343-354.
                                                                  and X-ray imaging. JOM. 2021;73(1):201-211.
               doi: 10.1007/s00138-006-0066-7
                                                                  doi: 10.1007/s11837-020-04291-5
            49.  Pandiyan V, Cui D, Le-Quang T, Deshpande P, Wasmer K,
               Shevchik S.  In situ quality monitoring in direct energy   59.  Forien JB, Calta NP, DePond PJ, Guss GM, Roehling TT,
               deposition process using co-axial process zone imaging   Matthews  MJ.  Detecting keyhole pore defects and
               and deep contrastive learning.  J  Manuf Process.   monitoring process signatures during laser powder bed
               2022;81:1064-1075.                                 fusion: A correlation between in situ pyrometry and ex situ
                                                                  X-ray radiography. Addit Manuf. 2020;35:101336.
               doi: 10.1016/j.jmapro.2022.07.033
                                                                  doi: 10.1016/j.addma.2020.101336
            50.  Asadi R, Queguineur A, Wiikinkoski O,  et al. Process   60.  Fleming TG, Rees DT, Marussi S, et al. In situ correlative
               monitoring by deep neural networks in directed energy   observation of humping-induced cracking in directed
               deposition: CNN-based detection, segmentation, and
               statistical  analysis  of melt pools.  Robot Comput Integr   energy deposition of nickel-based superalloys. Addit Manuf.
               Manuf. 2024;87:102710.                             2023;71:103579.
                                                                  doi: 10.1016/j.addma.2023.103579
               doi: 10.1016/j.rcim.2023.102710
                                                               61.  Wolff SJ, Webster S, Parab ND, et al. In-situ observations of
            51.  Li Y, Xiao W, Xiao H, et al. Enhanced molten-pool boundary
               stability for microstructure control using quasi-continuous-  directed energy deposition additive manufacturing using
               wave laser additive manufacturing.  J  Mater Res Technol.   high-speed X-ray imaging. JOM. 2021;73:189-200.
               2023;23:238-244.                                   doi: 10.1007/s11837-020-04469-x
               doi: 10.1016/j.jmrt.2022.12.172                 62.  Silveira  AC,  Fechte-Heinen  R,  Epp J.  Microstructure
                                                                  evolution during laser-directed energy deposition of tool
            52.  Gaikwad A, Chang T, Giera B, et al. In-process monitoring
               and prediction of droplet quality in droplet-on-demand   steel by in situ synchrotron X-ray diffraction. Addit Manuf.
               liquid metal jetting additive manufacturing using machine   2023;63:103408.
               learning. J Intell Manuf. 2022;33(7):2093-2117.     doi: 10.1016/j.addma.2023.103408


            Volume 1 Issue 1 (2025)                         22                             doi: 10.36922/esam.8548
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