Page 93 - MSAM-4-3
P. 93
Materials Science in Additive Manufacturing Interpretable GP melt track prediction
22. Xiang Y, Zhang S, Wei Z, et al. Forming and defect analysis doi: 10.1016/j.addma.2020.101470
for single track scanning in selective laser melting of 31. Tang M, Pistorius PC, Beuth JL. Prediction of lack-of-fusion
Ti6Al4V. Appl Phys A. 2018;124(10):685.
porosity for powder bed fusion. Addit Manuf. 2017;14:39-48.
doi: 10.1007/s00339-018-2056-9
doi: 10.1016/j.addma.2016.12.001
23. Hu Z, Nagarajan B, Song X, Huang R, Zhai W, Wei J. 32. Ning J, Mirkoohi E, Dong Y, Sievers DE, Garmestani H,
Formation of SS316L single tracks in micro selective laser Liang SY. Analytical modeling of 3D temperature distribution
melting: Surface, geometry, and defects. Adv Mater Sci Eng. in selective laser melting of Ti-6Al-4V considering part
2019;2019:9451406.
boundary conditions. J Manuf Process. 2019;44:319-326.
doi: 10.1155/2019/9451406
doi: 10.1016/j.jmapro.2019.06.013
24. Zhou H, Su H, Guo Y, et al. Formation and evolution 33. Promoppatum P, Yao SC, Pistorius PC, Rollett AD.
of surface morphology in overhang structure of IN718 A comprehensive comparison of the analytical and
superalloy fabricated by laser powder bed fusion. Acta numerical prediction of the thermal history and
Metall Sin. 2023;36(9):1433-1453.
solidification microstructure of inconel 718 products made
doi: 10.1007/s40195-023-01546-3 by laser powder-bed fusion. Engineering. 2017;3(5):685-694.
25. Li C, Guo YB, Zhao JB. Interfacial phenomena and doi: 10.1016/J.ENG.2017.05.023
characteristics between the deposited material and substrate
in selective laser melting Inconel 625. J Mater Process 34. Wang W, Liang SY. A 3D analytical modeling method for
keyhole porosity prediction in laser powder bed fusion. Int J
Technol. 2017;243:269-281.
Adv Manuf Technol. 2022;120(5-6):3017-3025.
doi: 10.1016/j.jmatprotec.2016.12.033
doi: 10.1007/s00170-022-08898-7
26. Khairallah SA, Anderson AT, Rubenchik A, King WE.
Laser powder-bed fusion additive manufacturing: Physics 35. Yang J, Han J, Yu H, et al. Role of molten pool mode on formability,
of complex melt flow and formation mechanisms of pores, microstructure and mechanical properties of selective laser
spatter, and denudation zones. Acta Mater. 2016;108:36-45. melted Ti-6Al-4V alloy. Mater Des. 2016;110:558-570.
doi: 10.1016/j.matdes.2016.08.036
doi: 10.1016/j.actamat.2016.02.014
36. Kamath C, El-dasher B, Gallegos GF, King WE, Sisto A.
27. Chen H, Lin X, Sun Y, Wang S, Zhu K, Dan B. Revealing
formation mechanism of end of process depression in laser Density of additively-manufactured, 316L SS parts using
powder bed fusion by multi-physics meso-scale simulation. laser powder-bed fusion at powers up to 400 W. Int J Adv
Virtual Phys Prototyp. 2024;19(1):e2326599. Manuf Technol. 2014;74(1-4):65-78.
doi: 10.1007/s00170-014-5954-9
doi: 10.1080/17452759.2024.2326599
37. Salimbeni H, Deisenroth M. Doubly stochastic variational
28. Yuan P, Gu D. Molten pool behaviour and its physical
mechanism during selective laser melting of TiC/AlSi10Mg inference for deep Gaussian processes. In: Advances in
nanocomposites: Simulation and experiments. J Phys D Appl Neural Information Processing Systems. United States:
Phys. 2015;48(3):035303. Morgan Kaufmann Publishers Inc.; 2017. p. 30.
doi: 10.48550/arXiv.1705.08933
doi: 10.1088/0022-3727/48/3/035303
38. Roth K, Pemula L, Zepeda J, Scholkopf B, Brox T, Gehler P.
29. Yuan W, Chen H, Cheng T, Wei Q. Effects of laser scanning
speeds on different states of the molten pool during selective Towards total recall in industrial anomaly detection.
laser melting: Simulation and experiment. Mater Des. In: IEEE/CVF Conference on Computer Vision and
2020;189:108542. Pattern Recognition (CVPR). United States: IEEE; 2022.
p. 14298-14308.
doi: 10.1016/j.matdes.2020.108542
doi: 10.1109/cvpr52688.2022.01392
30. Caprio L, Demir A, Previtali B. Observing molten pool
surface oscillations during keyhole processing in laser 39. Shojaie A, Fox EB. Granger causality: A review and recent
powder bed fusion as a novel method to estimate the advances. Annu Rev Stat Appl. 2022;9(1):289-319.
penetration depth. Addit Manuf. 2020;36:101470. doi: 10.1146/annurev-statistics-040120-010930
Volume 4 Issue 3 (2025) 19 doi: 10.36922/MSAM025200030

