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Materials Science in Additive Manufacturing Super-resolution method for L-PBF

References doi: 10.1016/j.ceramint.2022.04.033

1. Liu Y, Sing SL. A review of advances in additive 11. Li Y, Chen Z, Lin L, Liu S, Wang H, Zhang J. Effect of
manufacturing and the integration of high-performance pores on the stress field of high-frequency vibration of
polymers, alloys, and their composites. Mat Sci Addit TC17 specimen manufactured by laser additive. Int J Fract.
Manuf. 2023;2(3):1587. 2022;235(1):117-127.
doi: 10.36922/msam.1587 doi: 10.1007/s10704-021-00613-z
2. Sehhat MH, Sutton AT, Hung CH, et al. Plasma 12. Ince FD, Karna S, Zhang T, et al. An experimental process
spheroidization of gas-atomized 304L stainless steel powder parameter study on the identification of defects in additively
for laser powder bed fusion process. Mat Sci Addit Manuf. fabricated Al6061 with laser powder bed fusion. Mat Sci
2022;1(1):1. Addit Manuf. 2024;3(3):3652.
doi: 10.18063/msam.v1i1.1 doi: 10.36922/msam.3652
13. Brion DAJ, Shen M, Pattinson SW. Automated recognition
3. Fé-Perdomo IL, Ramos-Grez JA, Beruvides G, Mujica RA.
Selective laser melting: Lessons from medical devices and correction of warp deformation in extrusion additive
industry and other applications. Rapid Prototyp J. manufacturing. Addit Manuf. 2022;56:102838.
2021;27(10):1801-1830. doi: 10.1016/j.addma.2022.102838
doi: 10.1108/RPJ-07-2020-0151 14. Xiao Y, Wang X, Yang W, et al. Data-driven prediction of
future melt pool from built parts during metal additive
4. Lin X, Zhu K, Fuh JYH, Duan X. Metal-based additive manufacturing. Addit Manuf. 2024;93:104438.
manufacturing condition monitoring methods: From
measurement to control. ISA Trans. 2022;120:147-166. doi: 10.1016/j.addma.2024.104438
doi: 10.1016/j.isatra.2021.03.001 15. McCann R, Obeidi MA, Hughes C, et al. In-situ sensing,
process monitoring and machine control in laser powder
5. Wang Q, Lin X, Duan X, Yan R, Fuh JYH, Zhu K. Gaussian
process classification of melt pool motion for laser powder bed fusion: A review. Addit Manuf. 2021;45:102058.
bed fusion process monitoring. Mech Syst Signal Proc. doi: 10.1016/j.addma.2021.102058
2023;198:110440. 16. Taherkhani K, Ero O, Liravi F, Toorandaz S, Toyserkani E.
doi: 10.1016/j.ymssp.2023.110440 On the application of in-situ monitoring systems and
machine learning algorithms for developing quality
6. Wang J, Zhu R, Liu Y, Zhang L. Understanding melt pool assurance platforms in laser powder bed fusion: A review.
characteristics in laser powder bed fusion: An overview of J Manuf Process. 2023;99:848-897.
single-and multi-track melt pools for process optimization.
Adv Powder Mater. 2023;2(4):100137. doi: 10.1016/j.jmapro.2023.05.048
doi: 10.1016/j.apmate.2023.100137 17. Jiao W, Wang Q, Cheng Y, Zhang Y. End-to-end prediction
of weld penetration: A deep learning and transfer learning
7. Tao Z, Thanki A, Goossens L, Witvrouw A, Vrancken B, based method. J Manuf Process. 2021;63:191-197.
Dewulf W. Photodiode-based porosity prediction in laser
powder bed fusion considering inter-hatch and inter-layer doi: 10.1016/j.jmapro.2020.01.044
effects. J Mater Process Technol. 2024;332:118539. 18. Chen Z, Chen J, Feng Z. Welding penetration
doi: 10.1016/j.jmatprotec.2024.118539 prediction with passive vision system. J Manuf Process.
2018;36:224-230.
8. Yang W, Qiu Y, Liu W, Qiu X, Bai Q. Defect prediction in
laser powder bed fusion with the combination of simulated doi: 10.1016/j.jmapro.2018.10.009
melt pool images and thermal images. J Manuf Process. 19. Sampson R, Lancaster R, Sutcliffe M, Carswell D, Hauser C,
2023;106:214-222. Barras J. An improved methodology of melt pool monitoring
doi: 10.1016/j.jmapro.2023.10.006 of direct energy deposition processes. Opt Laser Technol.
2020;127:106194.
9. Mao Y, Lin X, Zhu K. Selective laser melting monitoring
based on the plume and its motion features. IEEE Trans doi: 10.1016/j.optlastec.2020.106194
Instrum Meas. 2024;73:1-14. 20. Barua S, Sparks T, Liou F. Development of low‐cost imaging
doi: 10.1109/TIM.2024.3432145 system for laser metal deposition processes. Rapid Prototyp
J. 2011;17(3):203-210.
10. Jin M, Wu X, Shao W, et al. Preparation and microstructural
evolution of spherical B-modified MoSi2 powders doi: 10.1108/13552541111124789
by induction plasma spheroidization. Ceram Int. 21. Zhang S, Fu T, Jahn A, Collet A, Schleifenbaum JH.
2022;48(14):20639-20647. Towards deep-learning-based image enhancement

Volume 3 Issue 4 (2024) 12 doi: 10.36922/msam.5585