AccScience Publishing / IJAMD / Volume 1 / Issue 2 / DOI: 10.36922/ijamd.3919
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ORIGINAL RESEARCH ARTICLE

AMTransformer: A Koopman theory-based transformer for learning additive manufacturing dynamics in laser processes

Suk Ki Lee1 ,  Hyunwoong Ko1*
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1 School of Manufacturing Systems and Networks, Ira A. Fulton Schools of Engineering, Arizona State University, Mesa, Arizona, United States of America
IJAMD 2024, 1(2), 76–91; https://doi.org/10.36922/ijamd.3919
Submitted: 12 June 2024 | Accepted: 9 August 2024 | Published: 2 September 2024
© 2024 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

Recent advancements in machine learning (ML) have shown unprecedented promise in understanding and predicting additive manufacturing (AM) dynamics. However, existing ML studies on AM often lack a comprehensive approach to address the multi-scale complexities inherent in AM processes and tend to employ context-specific methods. To address these limitations, we present a foundational method for formulating AM dynamics suitable for ML modeling. We then introduce a novel approach, the AMTransformer, designed to comprehend complex spatiotemporal dynamical dependencies among physical entities and their properties within the AM process. To enhance the understanding of AM dynamics, our method adapts Koopman’s theory to generate latent embeddings of AM states and their transitions, effectively extracting hidden features related to physical properties and dynamical dependencies. In addition, by utilizing the transformer’s attention mechanism, the proposed approach enhances the learning of non-local, non-linear dynamical dependencies across multiple scales. Our experiments, conducted using melt pool data from a laser powder bed fusion process, demonstrate that the AMTransformer outperforms traditional transformer and convolutional long short-term memory models. Specifically, the AMTransformer achieved structural similarity, mean absolute error, and accuracy metric values of 0.9206, 0.0009 mm2, and 92.73%, respectively. These results indicate the AMTransformer’s superior ability to predict future AM states, attributed to its improved learning of complex AM dynamics. By combining linear Koopman-based methods with non-linear transformer-based approaches, the AMTransformer significantly improves data-driven modeling for AM, providing a more comprehensive understanding of AM dynamics. Furthermore, the generalizability of the proposed method facilitates the expansion of the model’s scope and enhances its applicability across various fields.

Keywords
Additive manufacturing
Koopman theory
Laser AM dynamics
Machine learning
Predictive modeling
Transformer
Funding
This research was supported by Arizona State University startup funds (Award number: CC1379 PG14421), as well as by PADT and the Arizona State University Science and Technology Centers (Award number: AWD00037762).
Conflict of interest
Hyunwoong Ko is an 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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International Journal of AI for Materials and Design, Electronic ISSN: 3029-2573 Print ISSN: 3041-0746, Published by AccScience Publishing
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