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Engineering Science in
            Additive Manufacturing                                              Machine learning for biomedical metal AM



               powder  bed  fusion  additive  manufacturing.  Addit Manuf.   Model Predictive Control using time-series deep neural
               2020;31:100985.                                    networks. J Manuf Syst. 2025;80:412-424.
               doi: 10.1016/j.addma.2019.100985                   doi: 10.1016/j.jmsy.2025.03.009
            150. Meng L, McWilliams B, Jarosinski W,  et al. Machine   156. Li Z, Birbilis N. NSGAN: A  non-dominant sorting
               learning in additive manufacturing: A  review.  JOM.   optimisation-based generative adversarial design framework
               2020;72(6):2363-2377.                              for alloy discovery. NPJ Computat Mater. 2024;10(1):112.
               doi: 10.1007/s11837-020-04155-y                    doi: 10.1038/s41524-024-01294-7
            151. Ye D, Hong GS, Zhang Y, Zhu K, Fuh JYH. Defect detection   157. Griebler JJ, Tappan AS, Rogers SA, Grillet AM, Kopatz JW.
               in selective laser melting technology by acoustic signals   Printability criterion and filler characteristics model for
               with  deep  belief  networks.  Int J Adv Manuf Technol.
               2018;96(5):2791-2801.                              material extrusion additive manufacturing.  Addit Manuf.
                                                                  2025;99:104651.
               doi: 10.1007/s00170-018-1728-0
                                                                  doi: 10.1016/j.addma.2025.104651
            152. Armstrong AA, Pfeil A, Alleyne AG, Wagoner Johnson AJ.
               Process monitoring and control strategies in extrusion-  158. Ren W, Zhang YF, Wang WL, Ding SJ, Li N. Prediction and
               based bioprinting to fabricate spatially graded structures.   design of high hardness high entropy alloy through machine
               Bioprinting. 2021;21:e00126.                       learning. Mater Des. 2023;235:112454.
               doi: 10.1016/j.bprint.2020.e00126                  doi: 10.1016/j.matdes.2023.112454
            153. Liu  Y,  Wang L,  Brandt M. Model  predictive  control   159.  Trovato M, Belluomo L, Bici M, Prist M, Campana F, Cicconi P.
               of laser metal deposition.  Int J Adv Manuf Technol.   Machine  learning in  design  for additive manufacturing:
               2019;105(1):1055-1067.                             A  state-of-the-art discussion  for a  support tool  in  product
               doi: 10.1007/s00170-019-04279-9                    design lifecycle. Int J Adv Manuf Technol. 2025;137:2157-2180.
            154. Cao X, Ayalew B. Robust multivariable predictive control for      doi: 10.1007/s00170-025-15273-9
               laser-aided powder deposition processes. J Franklin Instit.   160. Gunasegaram DR, Barnard AS, Matthews MJ, et al. Machine
               2019;356(5):2505-2529.                             learning-assisted in-situ adaptive strategies for the control
               doi: 10.1016/j.jfranklin.2018.12.015               of defects and anomalies in metal additive manufacturing.
                                                                  Addit Manuf. 2024;81:104013.
            155. Chen YP, Karkaria V, Tsai YK,  et al. Real-time decision-
               making for Digital Twin in additive manufacturing with      doi: 10.1016/j.addma.2024.104013



































            Volume 1 Issue 4 (2025)                         31                         doi: 10.36922/ESAM025440031
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