Page 51 - ESAM-1-4
P. 51
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

