Page 95 - IJAMD-1-2

International Journal of AI for
Materials and Design
AMTransformer for process dynamics

By interpreting these dynamics, we aim to uncover how the 2. Ko H, Moon SK, Hwang J. Design for additive
attention mechanism enhances the model’s explanation of manufacturing in customized products. Int J Precis Eng
foundational dynamical dependencies in AM. In addition, Manuf. 2015;16:2369-2375.
we will explore incorporating a priori physical knowledge doi: 10.1007/s12541-015-0305-9
into the AMTransformer to improve the predictions and 3. Chua CK, Leong KF. 3D Printing and Additive Manufacturing:
interpretation. This understanding will help develop a new Principles and Applications (with Companion Media Pack)-of
method to improve alignment with physical phenomena. Rapid Prototyping. Singapore: World Scientific Publishing

Acknowledgments Company; 2014.
doi: 10.1142/9008
We wish to acknowledge the data provided by the National
Institute of Standards and Technology (NIST), which was 4. Gao W, Zhang Y, Ramanujan D, et al. The status, challenges,
and future of additive manufacturing in engineering.
instrumental in facilitating this research.
Comput Aided Des. 2015;69:65-89.
Funding doi: 10.1016/j.cad.2015.04.001
This research was supported by Arizona State University 5. King WE, Anderson AT, Ferencz RM, et al. Laser powder
startup funds (Award number: CC1379 PG14421), as well bed fusion additive manufacturing of metals; physics,
as by PADT and the Arizona State University Science and computational, and materials challenges. Appl Phys Rev.
Technology Centers (Award number: AWD00037762). 2015;2(4):041304.
doi: 10.1063/1.4937809
Conflict of interest
6. Bikas H, Stavropoulos P, Chryssolouris G. Additive
Hyunwoong Ko is an Editorial Board Member of this manufacturing methods and modelling approaches:
journal, but was not in any way involved in the editorial A critical review. Int J Adv Manuf Technol. 2016;83:389-405.
and peer-review process conducted for this paper, directly doi: 10.1007/s00170-015-7576-2
or indirectly. Separately, other authors declared that they
have no known competing financial interests or personal 7. Bourell DL, Frazier WE, Kuhn HA, Seifi M. Additive
Manufacturing Processes. Vol. 24. OH, USA: ASM
relationships that could have influenced the work reported International Novelty; 2020.
in this paper.
8. Kruth JP, Levy G, Klocke F, Childs T. Consolidation
Author contributions phenomena in laser and powder-bed based layered
manufacturing. CIRP Ann. 2007;56(2):730-759.
Conceptualization: All authors
Formal analysis: Suk Ki Lee doi: 10.1016/j.cirp.2007.10.004
Investigation: All authors 9. Ko H, Kim J, Lu Y, Shin D, Yang Z, Oh Y. Spatial-Temporal
Methodology: All authors Modeling using Deep Learning for Real-Time Monitoring
Writing – original draft: All authors of Additive Manufacturing. In: ASME 2022 International
Writing – review & editing: All authors Design Engineering Technical Conferences & Computers and
Information in Engineering Conference; 2022.
Ethics approval and consent to participate doi: 10.1115/DETC2022-91021

Not applicable. 10. Ko H, Lu Y, Yang Z, Ndiaye NY, Witherell P. A framework
driven by physics-guided machine learning for
Consent for publication process-structure-property causal analytics in additive
Not applicable. manufacturing. J Manuf Syst. 2023;67:213-228.
doi: 10.1016/j.jmsy.2022.09.010
Availability of data
11. Yan W, Lin S, Kafka OL, et al. Data-driven multi-scale
This study used publicly available data obtained through multi-physics models to derive process-structure-property
https://doi.org/10.6028/jres.125.027. relationships for additive manufacturing. Comput Mech.
2018;61:521-541.
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Volume 1 Issue 2 (2024) 89 doi: 10.36922/ijamd.3919