Page 87 - ESAM-1-1
P. 87
Engineering Science in
Additive Manufacturing ML in additive manufacturing
2024;154:104037. Sources, Techniques, Pipelines, and Applications. Germany:
Springer; 2023. p. 17-43.
doi: 10.1016/j.compind.2023.104037
53. Su J, Ng WL, An J, Yeong WY, Chua CK, Sing SL. 64. Dharmadhikari S, Menon N, Basak A. A reinforcement
Achieving sustainability by additive manufacturing: learning approach for process parameter optimization in
A state-of-the-art review and perspectives. Virtual Phys additive manufacturing. Addit Manuf. 2023;71:103556.
Prototyp. 2024;19(1):e2438899. doi: 10.1016/j.addma.2023.103556
doi: 10.1080/17452759.2024.2438899 65. Ogoke F, Farimani AB. Thermal control of laser powder bed
54. Zhao YF, Xie J, Sun L. On the data quality and imbalance fusion using deep reinforcement learning. Addit Manuf.
in machine learning-based design and manufacturing-a 2021;46:102033.
systematic review. Engineering. 2024;45:105-131. doi: 10.1016/j.addma.2021.102033
doi: 10.1016/j.eng.2024.04.024 66. Li C, Pan Y, Shi Y, Wang W. Optimization of process
55. Fullington D, Yangue E, Bappy MM, Liu C, Tian W. prediction models for hot-wire laser metal deposition using
Leveraging small-scale datasets for additive manufacturing transfer learning strategies based on simulation datasets. In:
process modeling and part certification: Current practice Welding in the World. Germany: Springer; 2025. p. 1-13.
and remaining gaps. J Manuf Syst. 2024;75:306-321. 67. Cheng L, Jiang Z, Wang H, et al. Low-rank adaptive transfer
doi: 10.1016/j.jmsy.2024.04.021 learning based for multi-label defect detection in laser
powder bed fusion. Opt Lasers Eng. 2025;184:108683.
56. Xie H, Hoskins D, Rowe K, Ju F. Transformer-based
offline printing strategy design for large format additive doi: 10.1016/j.optlaseng.2024.108683
manufacturing. J Comput Inf Sci Eng. 2024;25:021011. 68. Wu H, Fan ZM, Gan L. Feature transfer learning for fatigue
57. Zhu Q, Liu Z, Yan J. Machine learning for metal additive life prediction of additive manufactured metals with small
manufacturing: Predicting temperature and melt pool fluid samples. Fatigue Fract Eng Mater Struct. 2024;48:467-486.
dynamics using physics-informed neural networks. Comput doi: 10.1111/ffe.14497
Mech. 2021;67:619-635.
69. Kharate N, Anerao P, Kulkarni A, Abdullah M. Explainable
doi: 10.1007/s00466-020-01952-9 AI techniques for comprehensive analysis of the relationship
58. Li R, Jin M, Paquit VC. Geometrical defect detection for between process parameters and material properties in
additive manufacturing with machine learning models. FDM-Based 3D-printed biocomposites. J Manuf Mater
Mater Design. 2021;206:109726. Process. 2024;8(4):171.
doi: 10.1016/j.matdes.2021.109726 doi: 10.3390/jmmp8040171
59. Zhan Z, Li H. A novel approach based on the elastoplastic 70. Drakoulas G, Gortsas T, Polyzos E, Tsinopoulos S, Pyl L,
fatigue damage and machine learning models for life Polyzos D. An explainable machine learning-based
prediction of aerospace alloy parts fabricated by additive probabilistic framework for the design of scaffolds in
manufacturing. Int J Fatigue. 2021;145:106089. bone tissue engineering. Biomech Model Mechanobiol.
2024;23:987-1012.
doi: 10.1016/j.ijfatigue.2020.106089
doi: 10.1007/s10237-024-01817-7
60. Ko H, Lu Y, Yang Z, Ndiaye NY, Witherell P. A framework
driven by physics-guided machine learning for 71. Yoo YJ. Thermal imaging-based diagnostic process using
process-structure-property causal analytics in additive explainable artificial intelligence for 3D printing system. Soft
manufacturing. J Manuf Syst. 2023;67:213-228. Comput. 2024;28:1-12.
doi: 10.1016/j.jmsy.2022.09.010 doi: 10.1007/s00500-023-09530-w
61. Lu Y, Xie J, Safdar M, et al. An Overarching Quality Evaluation 72. Zhu Y, Wu X, Gotawala N, Higdon DM, Hang ZY. Thermal
Framework for Additive Manufacturing Digital Twin. United prediction of additive friction stir deposition through
States: IEEE; 2024. p. 676-682. Bayesian learning-enabled explainable artificial intelligence.
J Manuf Syst. 2024;72:1-15.
62. Liu B, Zhang T, Yu Y, Miao L. A data generation method with
dual discriminators and regularization for surface defect doi: 10.1016/j.jmsy.2023.10.015
detection under limited data. Comput Ind. 2023;151:103963.
73. Scime L, Beuth J. A multi-scale convolutional neural network
doi: 10.1016/j.compind.2023.103963 for autonomous anomaly detection and classification in a
laser powder bed fusion additive manufacturing process.
63. Safdar M, Lamouche G, Paul PP, Wood G, Zhao YF. Feature
engineering in additive manufacturing. In: Engineering of Add Manuf. 2018;24:273-286.
Additive Manufacturing Features for Data-Driven Solutions: 74. Shevchik SA, Kenel C, Leinenbach C, Wasmer K. Acoustic
Volume 1 Issue 1 (2025) 20 doi: 10.36922/ESAM025040004

