Page 84 - ESAM-1-1

Engineering Science in
Additive Manufacturing ML in additive manufacturing

research efforts and existing research questions. The Consent for publication
perspective identifies the increasing complexity of applied
ML models (e.g., large VLMs) which bring opportunities for Not applicable.
learning complex, multi-modal, and domain-specific tasks Availability of data
while also introducing increased data and computation
requirements. Advanced approaches in ML-driven AM Not applicable.
are discussed. These include data integration, knowledge References
transfer, feature engineering, adaptive sampling, data
augmentation, edge AI and federated learning, and 1. Gibson I, Rosen DW, Stucker B, et al. Additive Manufacturing
physics-informed learning. Emerging trends in applying Technologies. Vol 17. Germany: Springer; 2021.
ML models to AM concerns are identified (e.g., explainable 2. Wang C, Tan X, Tor S, Lim C. Machine learning in additive
learning). Opportunities to address data and modeling manufacturing: State-of-the-art and perspectives. Addit
challenges are presented. Research and development of Manuf. 2020;36:101538.
large AM datasets, AM foundation models, and efficient doi: 10.1016/j.addma.2020.101538
ML development approaches can support the applications
of advanced AI models. Similarly, the industrial 3. Naser AZ, Defersha F, Pei E, Zhao YF, Yang S. Toward
deployability of these AI-driven AM systems demands cost automated life cycle assessment for additive manufacturing:
A systematic review of influential parameters and framework
efficiency, reproducibility, reusability, data privacy, security, design. Sustain Prod Consum. 2023;41:253-274.
compliance to standards, explainability, and human-AI
teaming. The identified issues and research opportunities 4. Sun C, Wang Y, McMurtrey MD, Jerred ND, Liou F, Li J.
in this perspective can support the future integration of AI Additive manufacturing for energy: A review. Appl Energy.
in industrial AM processes. 2021;282:116041.
doi: 10.1016/j.apenergy.2020.116041
Acknowledgments
5. Faegh M, Ghungrad S, Oliveira JP, Rao P, Haghighi A.
None. A review on physics-informed machine learning for process-
structure-property modeling in additive manufacturing.
Funding J Manuf Processes. 2025;133:524-555.
This work is funded by McGill University Graduate doi: 10.1016/j.jmapro.2024.11.066
Excellence Fellowship Award (grant number 00157), Mitacs 6. Hu E, Seetoh IP, Lai CQ. Machine learning assisted
Accelerate program (grant number IT13369), McGill investigation of defect influence on the mechanical
Engineering Doctoral Award (MEDA), and National properties of additively manufactured architected materials.
Research Council Canada (grant number NRC INT-015-1). Int J Mech Sci. 2022;221:107190.

Conflict of interest doi: 10.1016/j.ijmecsci.2022.107190
7. Samadiani N, Barnard AS, Gunasegaram D, Fayyazifar N.
Yaoyao Fiona Zhao is the Editorial Board Member of this Best practices for machine learning strategies aimed at
journal but was not in any way involved in the editorial process parameter development in powder bed fusion
and peer-review process conducted for this paper, directly additive manufacturing. J Intell Manuf. 2024;1-41.
or indirectly. Separately, other authors declared that they doi: 10.1007/s10845-024-02490-4
have no known competing financial interests or personal
relationships that could have influenced the work reported 8. Shevchik SA, Masinelli G, Kenel C, Leinenbach C,
in this paper. Wasmer K. Deep learning for in situ and real-time quality
monitoring in additive manufacturing using acoustic
Author contributions emission. IEEE Transact Ind Inform. 2019;15(9):5194-5203.
Conceptualization: All authors doi: 10.1109/TII.2019.2910524
Data curation: Mutahar Safdar 9. Garland AP, White BC, Jensen SC, Boyce BL. Pragmatic
Visualization: Mutahar Safdar, Jiarui Xie generative optimization of novel structural lattice metamaterials
Writing – original draft: All authors with machine learning. Mater Design. 2021;203:109632.
Writing – review & editing: Yaoyao Fiona Zhao doi: 10.1016/j.matdes.2021.109632

Ethics approval and consent to participate 10. Wang Z, Yang W, Liu Q, et al. Data-driven modeling of
process, structure and property in additive manufacturing:
Not applicable. A review and future directions. J Manuf Processes.

Volume 1 Issue 1 (2025) 17 doi: 10.36922/ESAM025040004