Page 47 - IJAMD-1-1
P. 47
International Journal of AI
for Material and Design ML for quality improvement in L-PBF
2023;1274:012010. 41. Bland S, Aboulkhair NT. Reducing porosity in additive
manufacturing. Metal Powder Report. 2015;70(2):79-81.
doi: 10.1088/1757-899X/1274/1/012010
doi: 10.1016/j.mprp.2015.01.005
31. Liu Y, Sing SL. Review on the use of machine learning
techniques to optimize the processing of copper alloys 42. Gao B, Zhao H, Peng L, Sun Z. A review of research progress
in additive manufacturing. In: Asian Society for Precision in selective laser melting (SLM). Micromachines (Basel).
Engineering and Nanotechnology (ASPEN 2022). Singapore: 2023;14:57.
Research Publishing Services; 2022. p. 107-109.
doi: 10.3390/mi14010057
doi: 10.3850/978-981-18-6021-8_OR-01-0297.html
43. Solberg K, Guan S, Razavi N, et al. Fatigue of additively
32. Sabuj MR, Afshari SS, Liang X. Selective LASER melting part manufactured 316L stainless steel: The influence of porosity
quality prediction and energy consumption optimization. and surface roughness. Fatigue Fract Eng Mater Struct.
Meas Sci Technol. 2023;34(7):075902. 2019;42:2043-52.
doi: 10.1088/1361-6501/acc5a4 doi: 10.1111/ffe.13004
33. Uy M, Telford JK. Optimization by Design of Experiment 44. Tapia G, Elwany AH, Sang H. Prediction of porosity in
Techniques. Big Sky, MT, USA, IEEE Aerospace Conference metal-based additive manufacturing using spatial Gaussian
2009, p1-10. process models. Addit. Manuf. 2016;12:282-290.
doi: 10.1109/AERO.2009.4839625 doi: 10.1016/j.addma.2016.04.002
34. Goh GD, Huang X, Huang S, Thong JLJ, Seah JJ, Yeong WY. 45. Imani F, Gaikwad A, Montazeri M, Rao P, Yang H, Reutzel E.
Data imputation strategies for process optimization of laser Process mapping and in-process monitoring of porosity in
powder bed fusion of Ti6Al4V using machine learning. laser powder bed fusion using layerwise optical imaging.
MSAM. 2023;2(1):50. ASME J Manuf Sci Eng. 2018;140:101009.
doi: 10.36922/msam.50 doi: 10.1115/1.4039501
35. Wang C, Tan XP, Tor SB, Lim CS. Machine learning in 46. Hanzl H, Zetek M, Baka T, Kroupa T. The influence of
additive manufacturing: State-of-the-art and perspectives. processing parameters on the mechanical properties of SLM
Addit Manuf. 2020;36:101538. parts. Proc Eng. 2015;100:1405-1413.
doi: 10.1016/j.addma.2020.101538 doi: 10.1016/j.proeng.2015.01.456
36. Forien J, Calta N, DePond P, Guss G, Roehling T, 47. Maitra V, Shi J. Predictability assessment of as-built hardness
Matthews M. Detecting keyhole porosity and monitoring of Ti-6Al-4V alloy fabricated via laser powder bed fusion.
process signatures in additive manufacturing: An in situ Manufact Lett. 2023;35:785-796.
pyrometry and ex situ X-ray radiography correlation. Addit
Manuf. 2019;35. doi: 10.1016/j.mfglet.2022.12.001
doi: 10.1016/j.addma.2020.101336 48. Ravichander BB, Rahimzadeh A, Farhang B, Moghaddam NS,
Amerinatanzi A, Mehrpouya M. A prediction model for
37. Akbari P, Ogoke F, Kao NY, et al. MeltpoolNet: Melt pool additive manufacturing of inconel 718 superalloy. Appl Sci.
characteristic prediction in metal additive manufacturing 2021;11:8010.
using machine learning. Addit Manuf. 2022;55:102817.
doi: 10.3390/app11188010
doi: 10.1016/j.addma.2021.102817
49. Zhang T, Zhou X, Zhang P, et al. Hardness prediction of laser
38. Lee S, Peng J, Shin D, Choi YS. Data analytics approach for powder bed fusion product based on melt pool radiation
melt-pool geometries in metal additive manufacturing. Sci intensity. Materials (Basel). 2022;15(13):4674.
Technol Adv Mater. 2019;20(1):972-978.
doi: 10.3390/ma15134674
doi: 10.1080/14686996.2019.1669114
50. Fang Q, Tan Z, Li H, et al. In-situ capture of melt pool signature
39. Yang Z, Lu Y, Yeung H, Krishnamurty S. From scan strategy in selective laser melting using U-Net-based convolutional
to melt pool prediction: A Neighboring-effect modeling neural network. J Manuf Process. 2021;68:347-355.
method. ASME J Comput Inf Sci Eng. 2020;20(5):051001.
doi: 10.1016/j.jmapro.2021.05.052
doi: 10.1115/1.4047926
51. Taherkhani K, Eischer C, Toyserkani E. An unsupervised
40. Saunders R, Rawlings A, Birnbaum A, et al. Additive machine learning algorithm for in-situ defect-detection in
manufacturing melt pool prediction and classification via
multifidelity Gaussian process surrogates. Integr Mater laser powder-bed fusion. J Manuf Process. 2022;81:476-489.
Manuf Innov. 2022;11:497-515. doi: 10.1016/j.jmapro.2022.06.074
doi: 10.1007/s40192-022-00204-3 52. Huang DJ, Li H. A machine learning guided investigation
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