Machine learning for advancing laser powder bed fusion of stainless steel
Abd-Elaziem, Walaa; Elkatatny, Sally; Sebaey, Tamer A.; Darwish, Moustafa A.; Abd El-Baky, Marwa A.; hamada, Atef (2024-04-20)
Elsevier
20.04.2024
Walaa Abd-Elaziem, Sally Elkatatny, Tamer A. Sebaey, Moustafa A. Darwish, Marwa A. Abd El-Baky, Atef hamada, Machine learning for advancing laser powder bed fusion of stainless steel, Journal of Materials Research and Technology, Volume 30, 2024, Pages 4986-5016, ISSN 2238-7854, https://doi.org/10.1016/j.jmrt.2024.04.130
https://creativecommons.org/licenses/by/4.0/
© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
https://creativecommons.org/licenses/by/4.0/
© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
https://creativecommons.org/licenses/by/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202405063130
https://urn.fi/URN:NBN:fi:oulu-202405063130
Tiivistelmä
Abstract
In the dynamic landscape of advanced manufacturing, the confluence of laser powder bed fusion (LPBF) and machine learning (ML) has recently garnered significant attention in many applications. This review investigates the confluence of LPBF and ML, specifically within the specific domain of stainless steel. Firstly, it delves into LPBF principles, including an overview of critical process parameters and associated defects. Secondly, the paper meticulously addresses the distinct challenges posed by steel in additive manufacturing (AM), highlighting factors such as chemical composition, anisotropic microstructure, and oxide film formation, all of which require specialized considerations. Thirdly, the spotlight shifts to the pivotal role of ML, covering predictive modeling for process parameters, real-time defect detection, and quality control. This paper highlights recent advances, revealing how data-driven approaches can accelerate process understanding and part qualification. Eventually, this review offers insights into the future integration of ML in LPBF for steel, providing valuable perspectives on potential advancements in the field of AM.
In the dynamic landscape of advanced manufacturing, the confluence of laser powder bed fusion (LPBF) and machine learning (ML) has recently garnered significant attention in many applications. This review investigates the confluence of LPBF and ML, specifically within the specific domain of stainless steel. Firstly, it delves into LPBF principles, including an overview of critical process parameters and associated defects. Secondly, the paper meticulously addresses the distinct challenges posed by steel in additive manufacturing (AM), highlighting factors such as chemical composition, anisotropic microstructure, and oxide film formation, all of which require specialized considerations. Thirdly, the spotlight shifts to the pivotal role of ML, covering predictive modeling for process parameters, real-time defect detection, and quality control. This paper highlights recent advances, revealing how data-driven approaches can accelerate process understanding and part qualification. Eventually, this review offers insights into the future integration of ML in LPBF for steel, providing valuable perspectives on potential advancements in the field of AM.
Kokoelmat
- Avoin saatavuus [37575]
Ellei muuten mainita, aineiston lisenssi on https://creativecommons.org/licenses/by/4.0/