Proceedings of the 4th International Conference on Key Enabling Technologies (KEYTECH 2024)

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A Review of Machine Learning Methods for Process Parameter Optimization in Laser Powder Bed Fusion

Authors
Danish Inam1, *, Intizar Ali1, Ali Akbar Shah Syed1, Inam Ul Ahad1
1School of Mechanical and Manufacturing Engineering, Dublin City University, Dublin, Ireland
*Corresponding author. Email: danish.inam@dcu.ie
Corresponding Author
Danish Inam
Available Online 24 December 2024.
DOI
10.2991/978-94-6463-602-4_40How to use a DOI?
Keywords
LPBF; Machine Learning; Process Parameter Optimization; In-situ Monitoring; Part Quality
Abstract

Laser Powder Bed Fusion (LPBF) is an additive manufacturing technique that has gained significant attention due to its ability to produce complex geometries with high precision. However, the optimization of process parameters to achieve desired part quality remains a challenge. This paper presents a systematic review of machine learning (ML) methods applied to process parameter optimization in LPBF. The review covers key influential input parameters, in-situ sensors used in LPBF processes, and various ML approaches, including artificial neural networks (ANNs), and supervised, and unsupervised learning techniques. The paper discusses the strengths and weaknesses of different ML approaches, highlighting their potential to improve the efficiency and quality of LPBF processes. Additionally, the review identifies challenges and future directions in this field, emphasizing the need for further research to develop more accurate and robust optimization strategies.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 4th International Conference on Key Enabling Technologies (KEYTECH 2024)
Series
Atlantis Highlights in Engineering
Publication Date
24 December 2024
ISBN
978-94-6463-602-4
ISSN
2589-4943
DOI
10.2991/978-94-6463-602-4_40How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Danish Inam
AU  - Intizar Ali
AU  - Ali Akbar Shah Syed
AU  - Inam Ul Ahad
PY  - 2024
DA  - 2024/12/24
TI  - A Review of Machine Learning Methods for Process Parameter Optimization in Laser Powder Bed Fusion
BT  - Proceedings of the 4th International Conference on Key Enabling Technologies (KEYTECH 2024)
PB  - Atlantis Press
SP  - 302
EP  - 311
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-602-4_40
DO  - 10.2991/978-94-6463-602-4_40
ID  - Inam2024
ER  -