AccScience Publishing / ESAM / Volume 1 / Issue 1 / DOI: 10.36922/ESAM025040004
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PERSPECTIVE ARTICLE

Advancing machine learning in additive manufacturing: Perspectives on data challenges, model development, and industrial adoption

Mutahar Safdar1† ,  Jiarui Xie1† ,  Yaoyao Fiona Zhao1*
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1 Department of Mechanical Engineering, Additive Design and Manufacturing Laboratory,McGill University, Montreal, Canada
†These authors contributed equally to this work.
ESAM 2025, 1(1), 025040004 https://doi.org/10.36922/ESAM025040004
Received: 24 January 2025 | Revised: 3 March 2025 | Accepted: 6 March 2025 | Published online: 19 March 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Over the past decade, 100 of scientific studies have harnessed statistical artificial intelligence, specifically machine learning (ML), to address the existing challenges to process repeatability and part quality in additive manufacturing (AM). ML has also been applied to support structure and material design for AM. This rapidly expanding field at the intersection of two growing disciplines provides opportunities to mature AM technology in the industry. There have been numerous review articles and survey reports to summarize ML applications at design, processes, structure, and property phases. While ML models, data handling, and learning techniques for AM concerns have been summarized in these articles, no work has specifically focused on the core ML challenges of data scarcity and model complexity – two important aspects to develop ML models for the AM industry. This perspective highlights existing data and model challenges and discusses the opportunities to address them through advanced techniques in data science and ML. By enhancing the usefulness of AM datasets and by leveraging the strengths of cutting-edge ML models, the research progress at the intersection of ML and AM can be effectively translated into real-world industrial applications making the deployment of ML models in the industry much easier.

Keywords
Additive manufacturing
Artificial intelligence
Machine learning
Data scarcity
Model complexity
Perspective
Industrial integration
Funding
This work is funded by McGill University Graduate Excellence Fellowship Award (grant number 00157), Mitacs Accelerate program (grant number IT13369), McGill Engineering Doctoral Award (MEDA), and National Research Council Canada (grant number NRC INT-015-1).
Conflict of interest
Yaoyao Fiona Zhao is the Editorial Board Member of this journal but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. Separately, other authors declared that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
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Engineering Science in Additive Manufacturing, Published by AccScience Publishing
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