Page 35 - ESAM-1-1
P. 35

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



            185. Yeung H, Lane B, Fox J. Part geometry and conduction-  using inkjet-printed additive manufacturing technology
               based laser power control for powder bed fusion additive   for metal crack characterization.  Smart  Mater  Struct.
               manufacturing. Addit Manuf. 2019;30:100844.        2024;33(11):115042.
               doi: 10.1016/j.addma.2019.100844                   doi: 10.1088/1361-665X/ad8798
            186. Gunasegaram DR, Barnard AS, Matthews MJ, et al. Machine   191. Mehta M, Shao CH. Federated learning-based semantic
               learning-assisted in-situ adaptive strategies for the control   segmentation for pixel-wise defect detection in additive
               of defects and anomalies in metal additive manufacturing.   manufacturing. J Manuf Syst. 2022;64:197-210.
               Addit Manuf. 2024;81:104013.
                                                                  doi: 10.1016/j.jmsy.2022.06.010
               doi: 10.1016/j.addma.2024.104013
                                                               192. Wang JM, Kim ES, Kim HS, Lee BJ. A  machine learning
            187. Freeman F, Chechik L, Thomas B, Todd I. Calibrated closed-  approach for predicting evaporation-induced composition
               loop control to reduce the effect of geometry on mechanical   variability in directed energy deposition  in-situ alloying.
               behaviour in directed energy deposition.  J  Mater Process   Addit Manuf. 2024;92:104384.
               Technol. 2023;311:117823.
                                                                  doi: 10.1016/j.addma.2024.104384
               doi: 10.1016/j.jmatprotec.2022.117823
                                                               193. Tang YF, Dehaghani MR, Sajadi P, Wang GG. Selecting
            188. Lam CT, Hoang TN, Low BKH, Jaillet P. Model Fusion   subsets of source data for transfer learning with applications
               for Personalized Learning. In:  Proceedings of the   in metal additive manufacturing. J Intell Manuf. 2024.
               38  International Conference on Machine Learning; 2021.
                 th
                                                                  doi: 10.1007/s10845-024-02402-6
            189. Roca JB, Vaishnav P, Fuchs ERH, Morgan MG. Policy needed   194. Danry V, Guzelis C, Huang LD, Gershenfeld N, Maes P.
               for additive manufacturing. Nat Mater. 2016;15(8):815-818.
                                                                  From words to worlds: Exploring generative 3D models in
               doi: 10.1038/nmat4658                              design and fabrication. 3D Print Addit Manuf. 2024;12.
            190. Xie XY, Wang HD, Dong LH, et al. An RFID smart structure      doi: 10.1089/3dp.2023.0309















































            Volume 1 Issue 1 (2025)                         29                             doi: 10.36922/esam.8548
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