Page 46 - IJAMD-1-1
P. 46
International Journal of AI
for Material and Design ML for quality improvement in L-PBF
gradient porosity fabricated by selective laser melting. 2021;32:2353-2373.
J Mater Eng Perform. 2010;19(5):666-671.
doi: 10.1007/s10845-021-01785-0
doi: 10.1007/s11665-009-9535-2.
20. Srinivas M, Sucharitha G, Matta A, Chatterjee P, editors.
st
9. Gao W, Zhang Y, Ramanujan D, et al. The status, challenges, Machine Learning Algorithms and Applications. 1 ed. United
and future of additive manufacturing in engineering. States: Wiley; 2021.
Comput Aided Des. 2015;69:65-89.
doi: 10.1002/9781119769262
doi: 10.1016/j.cad.2015.04.001
21. Van Engelen JE, Hoos HH. A survey on semi-supervised
10. Pereira T, Kennedy JV, Potgieter J. A comparison of learning. Mach Learn. 2020;109(2):373-440.
traditional manufacturing vs additive manufacturing, the doi: 10.1007/s10994-019-05855-6
best method for the job. Proc Manuf. 2019;30:11-18.
22. Bradford E, Schweidtmann A, Lapkin A. Efficient
doi: 10.1016/j.promfg.2019.02.003
multiobjective optimization employing Gaussian processes,
11. Fotovvati B, Balasubramanian M, Asadi E. Modeling and spectral sampling, and a genetic algorithm. J Glob Optim.
optimization approaches of laser-based powder-bed fusion 2018;71:407-438.
process for Ti-6Al-4V alloy. Coatings. 2020;10(11):1104.
doi: 10.1007/s10898-018-0609-2
doi: 10.3390/coatings10111104
23. Liashchynskyi P, Liashchynskyi P. Grid search, random
12. Khaimovich AI, Stepanenko IS, Smelov VG. Optimization search, genetic algorithm: A big comparison for NAS[J].
of selective laser melting by evaluation method of multiple arXiv preprint arXiv:1912.06059; 2019.
quality characteristics. IOP Conf Ser Mater Sci Eng. doi: 10.48550/arXiv.1912.06059
2018;302(1):012067.
24. Shi T, Sun J, Li J, Qian G, Hong Y. Machine learning
doi: 10.1088/1757-899X/302/1/012067
based very-high-cycle fatigue life prediction of AlSi10Mg
13. Wang C, Tan XP, Tor SB, Lim CS. Machine learning in alloy fabricated by selective laser melting. Int J Fatigue.
additive manufacturing: State-of-the-art and perspectives. 2023;171:107585.
Addit Manuf. 2020;36:101538.
doi: 10.1016/j.ijfatigue.2023.107585
doi: 10.1016/j.addma.2020.101538
25. Bergstra J, Bengio Y. Random search for hyper-parameter
14. Janík S, Szabó P, Mĺkva M, Mareček-Kolibiský M. Effective optimization. J Mach Learn Res. 2012;13:281-305.
data utilization in the context of industry 4.0 technology doi: 10.5555/2188385.2188395
integration. Appl Sci. 2022;12(20):10517.
26. Shahriari B, Swersky K, Wang Z, et al. Taking the human out
doi: 10.3390/app122010517
of the loop: A review of Bayesian optimization. Proc IEEE.
15. Jordan MI, Mitchell TM. Machine learning: Trends, 2016;104(1):148-175.
perspectives, and prospects. Science. 2015;349(6245):255-260.
doi: 10.1109/JPROC.2015.2494218
doi: 10.1126/science.aaa8410
27. Rong-Ji W, Xin-Hua L, Qing-Ding W, Lingling W.
16. Huang DJ, Li H. A machine learning guided investigation Optimizing process parameters for selective laser sintering
of quality repeatability in metal laser powder bed fusion based on neural network and genetic algorithm. Int J Adv
additive manufacturing. Mater Des. 2021;203:109606. Manuf Technol. 2009;42:1035-1042.
doi: 10.1016/j.matdes.2021.109606 doi: 10.1007/s00170-008-1669-0
17. Goh GD, Sing SL, Yeong WY. A review on machine learning 28. Zouhri W, Dantan JY, Häfner B, et al. Optical process
in 3D printing: Applications, potential, and challenges. Artif monitoring for laser-powder bed fusion (L-PBF). CIRP J
Intell Rev. 2021;54:63-94. Manufact Sci Technol. 2020;31:607-617.
doi: 10.1007/s10462-020-09876-9 doi: 10.1016/j.cirpj.2020.09.001
18. Scime L, Beuth J. Anomaly detection and classification 29. Wang C, Tan X, Liu E, Tor SB. Process parameter
in a laser powder bed additive manufacturing process optimization and mechanical properties for additively
using a trained computer vision algorithm. Addit Manuf. manufactured stainless steel 316L parts by selective electron
2018;19:114-126. beam melting. Mater Des. 2018;147:157-166.
doi: 10.1016/j.addma.2017.11.009 doi: 10.1016/j.matdes.2018.02.059
19. Sanchez S, Rengasamy D, Hyde CJ, Figueredo GP, Rothwell B. 30. Chadwick AF, Voorhees PW. The effects of melt pool
Machine learning to determine the main factors Affecting geometry and scan strategy on microstructure development
creep rates in laser powder bed fusion. J Intell Manufact. during additive manufacturing. IOP Conf Ser Mater Sci Eng.
Volume 1 Issue 1 (2024) 40 https://doi.org/10.36922/ijamd.2301

