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Engineering Science in
            Additive Manufacturing                                              Machine learning for biomedical metal AM



               doi: 10.1038/s41524-024-01353-z                    Prototyp. 2020;15(1):106-119.
            109. Wang ZL, Ogawa T, Adachi Y. Influence of algorithm      doi: 10.1080/17452759.2019.1692673
               parameters of Bayesian optimization, genetic algorithm,   119. Cai Y, Wang Y, Chen H, Xiong J. Searching optimal process
               and particle swarm optimization on their optimization   parameters for desired layer geometry in wire-laser directed
               performance. Adv Theory Simul. 2019;2(10):1900110.
                                                                  energy deposition based on machine learning. Virtual Phys
               doi: 10.1002/adts.201900110                        Prototyp. 2024;19(1):e2352066.
            110. Palm N, Landerer M, Palm H. Gaussian process regression      doi: 10.1080/17452759.2024.2352066
               based multi-objective Bayesian optimization for power   120. Heiss A, Thatikonda VS, Klotz UE. Multi-objective
               system design. Sustainability. 2022;14(19):12777.  optimization of LPBF manufacturing with Zn-4Al-1Cu alloy
               doi: 10.3390/su141912777                           for technical applications. J Manuf Process. 2025;134:193-206.
            111. Moradi A, Tajalli S, Mosallanejad MH, Saboori A. Intelligent      doi: 10.1016/j.jmapro.2024.12.049
               laser-based metal additive manufacturing: A  review on   121. Peng S, Li T, Zhao J,  et  al. Towards energy and material
               machine learning for process optimization and property   efficient laser cladding process: Modeling and optimization
               prediction. Int J Adv Manuf Technol. 2024;136(2):527-560.  using a hybrid TS-GEP algorithm and the NSGA-II. J Clean
               doi: 10.1007/s00170-024-14858-0                    Prod. 2019;227:58-69.
            112. Narayana PL, Kim JH, Lee J,  et al. Optimization of      doi: 10.1016/j.jclepro.2019.04.187
               process parameters for direct energy deposited Ti-6Al-4V   122. Hu Z, Huang C, Xie L, Hua L, Yuan Y, Zhang LC.
               alloy using neural networks.  Int J  Adv  Manuf Technol.   Machine  learning  assisted  quality  control  in metal
               2021;114(11):3269-3283.                            additive  manufacturing:  A  review.  Adv Powder Mater.
               doi: 10.1007/s00170-021-07115-1                    2025;4(6):100342.
            113. Nguyen DS, Park HS, Lee CM. Optimization of selective      doi: 10.1016/j.apmate.2025.100342
               laser melting process parameters for Ti-6Al-4V alloy   123. Gerdes S, Gaikwad A, Ramesh S, Rivero IV, Tamayol  A,
               manufacturing using deep learning.  J  Manuf Process.   Rao P. Monitoring and control of biological additive
               2020;55:230-235.                                   manufacturing using machine learning.  J  Intell Manuf.
               doi: 10.1016/j.jmapro.2020.04.014                  2024;35(3):1055-1077.
            114. Gan Z, Li H, Wolff SJ, et al. Data-driven microstructure and      doi: 10.1007/s10845-023-02092-6
               microhardness  design  in  additive  manufacturing  using  a   124. Pereira AG, Barbosa GF, Filho MG, Shiki SB, Silva AL.
               self-organizing map. Engineering. 2019;5(4):730-735.  Quality control in extrusion-based additive manufacturing:
               doi: 10.1016/j.eng.2019.03.014                     A  review  of  machine  learning  approaches.  IEEE  Trans
                                                                  Cybern. 2025;55(6):2522-2534.
            115. Tapia G, Khairallah S, Matthews M, King WE, Elwany A.
               Gaussian process-based surrogate modeling framework      doi: 10.1109/tcyb.2025.3558515
               for process planning in laser powder-bed fusion additive   125. Khanzadeh M, Chowdhury S, Tschopp MA, Doude HR,
               manufacturing of 316L stainless steel.  Int J Adv Manuf   Marufuzzaman M, Bian L. In-situ monitoring of melt pool
               Technol. 2018;94(9):3591-3603.                     images for porosity prediction in directed energy deposition
               doi: 10.1007/s00170-017-1045-z                     processes. IISE Trans. 2019;51(5):437-455.
            116. Buchner C, Riedle B, Krauß J, et al. Machine learning-driven      doi: 10.1080/24725854.2017.1417656
               multi-objective parameter optimization for sustainable,   126. Yang T, Mazumder S, Jin Y, et al. A review of diagnostics
               efficient, and high-quality ultrasonic wire bonding. J Intell   methodologies for metal additive manufacturing processes
               Manuf. 2025.                                       and products. Materials (Basel). 2021;14(17):4929.
               doi: 10.1007/s10845-025-02615-3                    doi: 10.3390/ma14174929
            117. Wang S, Xia P, Gong F, Zeng Q, Chen K, Zhao Y. Multi   127. Zheng L, Zhang Q, Cao H,  et al. Melt pool boundary
               objective optimization of recycled aggregate concrete   extraction and its width prediction from infrared images in
               based on explainable machine learning.  J  Clean Prod.   selective laser melting. Mater Des. 2019;183:108110.
               2024;445:141045.
                                                                  doi: 10.1016/j.matdes.2019.108110
               doi: 10.1016/j.jclepro.2024.141045
                                                               128. Mohr G, Altenburg SJ, Ulbricht A,  et al.  In-situ defect
            118. Meng L, Zhao J, Lan X, Yang H, Wang Z. Multi-objective   detection in laser powder bed fusion by using thermography
               optimisation of bio-inspired lightweight sandwich   and optical tomography-comparison to computed
               structures based on selective laser melting.  Virtual  Phys   tomography. Metals. 2020;10(1):103.


            Volume 1 Issue 4 (2025)                         29                         doi: 10.36922/ESAM025440031
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