Abstract
In recent decades, laser additive manufacturing has seen rapid development and has been applied to various fields, including the aerospace, automotive, and biomedical industries. However, the residual stresses that form during the manufacturing process can lead to defects in the printed parts, such as distortion and cracking. Therefore, accurately predicting residual stresses is crucial for preventing part failure and ensuring product quality. This critical review covers the fundamental aspects and formation mechanisms of residual stresses. It also extensively discusses the prediction of residual stresses utilizing experimental, computational, and machine learning methods. Finally, the review addresses the challenges and future directions in predicting residual stresses in laser additive manufacturing.
Recommended Citation
S. H. Wu et al., "Experimental, Computational, and Machine Learning Methods for Prediction of Residual Stresses in Laser Additive Manufacturing: A Critical Review," Materials, vol. 17, no. 7, article no. 1498, MDPI, Apr 2024.
The definitive version is available at https://doi.org/10.3390/ma17071498
Department(s)
Mechanical and Aerospace Engineering
Publication Status
Open Access
Keywords and Phrases
computational method; experimental measurement; machine learning; residual stresses
International Standard Serial Number (ISSN)
1996-1944
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Apr 2024
Comments
Directorate for Biological Sciences, Grant CMMI 1625736