Page 74 - MSAM-4-3

Materials Science in Additive Manufacturing Bead geometry prediction in laser-arc AM

process regression: Modelling, exploring, and exploiting 48. Wang R, Cheng MN, Loh YM, Wang C, Fai Cheung C.
functions. J Math Psychol. 2018;85:1-16. Ensemble learning with a genetic algorithm for surface
roughness prediction in multi-jet polishing. Exp Syst Appl.
doi: 10.1016/j.jmp.2018.03.001
2022;207:118024.
45. Shapley LS. Quota solutions of n-person games. In: Harold doi: 10.1016/j.eswa.2022.118024
William K, Albert William T, editors. Contributions to the
Theory of Games. Vol. 2. United States: Princeton University 49. Chung J, Shen B, Kong ZJ. Anomaly detection in additive
Press; 1953. p. 343-360. manufacturing processes using supervised classification
with imbalanced sensor data based on generative adversarial
46. Liu CA, Kuo BS. Model averaging in predictive regressions. network. J Intell Manuf. 2024;35(5):2387-2406.
Econom J. 2016;19(2):203-231.
doi: 10.1007/s10845-023-02163-8
doi: 10.1111/ectj.12063
50. Chua C, Liu Y, Williams RJ, Chua CK, Sing SL. In-process
47. Mu G, Wei Q, Xu Y, Zhang H, Zhang J, Li Q. Capacity and post-process strategies for part quality assessment in
estimation for lithium-ion batteries based on heterogeneous metal powder bed fusion: A review. J Manuf Syst. 2024;73:
stacking model with feature fusion. Energy. 2024;313:133881. 75-105.
doi: 10.1016/j.energy.2024.133881 doi: 10.1016/j.jmsy.2024.01.004

Volume 4 Issue 3 (2025) 17 doi: 10.36922/MSAM025220036