Page 54 - IJAMD-1-3
P. 54
International Journal of AI for
Materials and Design
Metal AM porosity prediction using ML
24. Estalaki SM, Lough CS, Landers RG, Kinzel EC, Luo T. Synthetic minority over-sampling technique. J Artif Intell
Predicting defects in laser powder bed fusion using in-situ Res. 2002;16:321-357.
thermal imaging data and machine learning. Addit Manuf.
2022;58:103008. doi: 10.1613/jair.953
36. Wilson DL. Asymptotic properties of nearest neighbor
doi: 10.1016/j.addma.2022.103008
rules using edited data. IEEE Trans Syst Man Cybern.
25. Gordon JV, Narra SP, Cunningham RW, et al. Defect 1972;2(3):408-421.
structure process maps for laser powder bed fusion additive
manufacturing. Addit Manuf. 2020;36:101552. doi: 10.1109/TSMC.1972.4309137
37. Torgo L, Ribeiro RP, Pfahringer B, Branco P. Smote
doi: 10.1016/j.addma.2020.101552
for regression. In: Portuguese Conference on Artificial
26. Du Plessis A. Effects of process parameters on porosity in Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg;
laser powder bed fusion revealed by X-ray tomography. 2013. p. 378-389.
Addit Manuf. 2019;30:100871.
38. Christ M, Braun N, Neuffer J, Kempa‑Liehr AW. Time
doi: 10.1016/j.addma.2019.100871 series feature extraction on basis of scalable hypothesis tests
27. Kan WH, Nadot Y, Foley M, Ridosz L, Proust G, Cairney JM. (tsfresh-a python package). Neurocomputing. 2018;307:72-77.
Factors that affect the properties of additively-manufactured doi: 10.1016/j.neucom.2018.03.067
AlSi10Mg: Porosity versus microstructure. Addit Manuf.
2019;29:100805. 39. Peng CYJ, Lee KL, Ingersoll GM. An introduction to logistic
regression analysis and reporting. J Educ Res. 2002;96(1):3-14.
doi: 10.1016/j.addma.2019.100805
doi: 10.1080/00220670209598786
28. Hojjatzadeh SMH, Parab ND, Guo Q, et al. Direct
observation of pore formation mechanisms during LPBF 40. Mahato V, Cunningham P. A Case‑study on the Impact
additive manufacturing process and high energy density of Dynamic Time Warping in Time Series Regression. In:
rd
laser welding. Int J Mach Tools Manuf. 2020;153:103555. 3 ECML/PKDD Workshop on Advanced Analytics and
Learning on Temporal Data. 2018.
doi: 10.1016/j.ijmachtools.2020.103555
doi: 10.48550/arXiv.2010.05270
29. Vastola G, Pei QX, Zhang YW. Predictive model for porosity
in powder-bed fusion additive manufacturing at high beam 41. Mahato V, Obeidi MA, Brabazon D, Cunningham P. An
energy regime. Addit Manuf. 2018;22:817-822. evaluation of classification methods for 3d printing time-
series data. IFAC-PapersOnLine. 2020;53(2):8211-8216.
doi: 10.1016/j.addma.2018.05.042
doi: 10.1016/j.ifacol.2020.12.1992
30. Sun S, Brandt M, Easton MJL. Powder bed fusion processes:
An overview. Laser Additive Manufacturing. Sawston, UK: 42. Badiane M, Cunningham P. An empirical evaluation of
Woodhead Publishing; 2017. p. 55-77. kernels for time series. Artif Intell Rev. 2022;55(3):1803-1820.
31. Mahato V, Obeidi MA, Brabazon D, Cunningham P. An doi: 10.1007/s10462-021-10050-y
evaluation of classification methods for 3d printing time- 43. Safavian SR, Landgrebe D. A survey of decision tree classifier
series data. IFAC-PapersOnLine. 2020;53(2):8211-8216. methodology. IEEE Trans Syst Man Cybern. 1991;21(3):
doi: 10.1016/j.ifacol.2020.12.1992 660-674.
32. Grasso M, Colosimo BM. Process defects and in situ doi: 10.1109/21.97458
monitoring methods in metal powder bed fusion: A review. 44. Breiman L. Random forests. Mach Learn. 2001;45:5-32.
Meas Sci Technol. 2017;28(4):044005.
doi: 10.1023/A:1010950718922
doi: 10.1088/1361-6501/aa5c4f
45. Biau G, Scornet E. A random forest guided tour. Test.
33. Li Y, Gu D. Thermal behavior during selective laser melting of 2016;25:197-227.
commercially pure titanium powder: Numerical simulation
and experimental study. Addit Manuf. 2014;1:99-109. doi: 10.1007/s11749-016-0481-7
doi: 10.1016/j.addma.2014.09.001 46. Geurts P, Ernst D, Wehenkel L. Extremely randomized trees.
Mach Learn. 2006;63:3-42.
34. Yadav P, Rigo O, Arvieu C, Le Guen E, Lacoste E. In situ
monitoring systems of the SLM process: On the need to doi: 10.1007/s10994-006-6226-1
develop machine learning models for data processing. 47. Freund Y, Schapire RE. A decision‑theoretic generalization
Crystals. 2020;10(6):524. of on-line learning and an application to boosting. J Comput
doi: 10.3390/cryst10060524 Syst Sci. 1997;55(1):119-139.
35. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: doi: 10.1007/3-540-59119-2_166
Volume 1 Issue 3 (2024) 48 doi: 10.36922/ijamd.4812

