Experimental, Computational, and Machine Learning Methods for Prediction of Residual Stresses in Laser Additive Manufacturing - A Critical Review

Aug 5, 2026

Upload by Hazel

Laser additive manufacturing uses focused lasers to build components layer by layer, enabling high design freedom and complex internal geometries. Residual stresses formed during processing can cause distortion, cracking, delamination, and early failure, undermining fatigue strength and overall part integrity. This critical review synthesizes fundamental mechanisms of residual stress formation and evaluates prediction approaches across experimental measurement, computational modeling, and machine learning. It also discusses key challenges and future research directions for accurate residual-stress prediction in laser-based AM.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages27

Reading Time1 h 8 min

AccessFree to download

Abstract

Introduction

Laser additive manufacturing processes and techniques

Defects, residual stress formation, and implications

Review Scope and Prediction Methods

Experimental methods

Computational methods

Machine learning methods

Challenges and Future Directions

Missouri University of Science and Technology Scholars' Mine | Mechanical and Aerospace Engineering Faculty Research & Creative Works | Mechanical and Aerospace Engineering | | --- | --- | | 01 Apr 2024
Experimental, Computational, and Machine Learning Methods for Prediction of Residual Stresses in Laser Additive Manufacturing: A Critical Review
Sung Heng Wu Usman Tariq Ranjit Joy Todd Sparks
[et. al. For a complete list of authors](et. al. For a complete list of authors), see [https://](https://)scholarsmine. mst. edu/mec_ aereng_facwork/5486
Follow this and additional works at: [https://scholarsmine.mst.edu/mec_aereng_facwork](https://scholarsmine.mst.edu/mec_aereng_facwork)
Part of the Aerospace Engineering Commons, and the Mechanical Engineering Commons | | 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](https://doi.org/10.3390/ma17071498) This work is licensed under a Creative Commons Attribution 4.0 License. This Article-Journal is brought to you for free and open access by Scholars' Mine. It has been accepted for inclusion in Mechanical and Aerospace Engineering Faculty Research & Creative Works by an authorized administrator of Scholars' Mine. This work is protected by U. S. Copyright Law. Unauthorized use including reproduction for redistribution requires the permission of the copyright holder. For more information, please contact [scholarsmine@mst.edu](scholarsmine@mst.edu). materials Review Experimental, Computational, and Machine Learning Methods for Prediction of Residual Stresses in Laser Additive Manufacturing: A Critical Review Sung-Heng Wu 1, Usman Tariq 1, Ranjit Joy 1, Todd Sparks 2, Aaron Flood 2 and Frank Liou 1, * Citation: Wu, S.-H.; Tariq, U.; Joy, R.; Sparks, T.; Flood, A.; Liou, F. Experimental, Computational, and Machine Learning Methods for Prediction of Residual Stresses in Laser Additive Manufacturing: A Critical Review. Materials 2024, 17, 1498. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)ma17071498 Academic Editors: Jan Akmal and Mika Salmi Received: 20 February 2024 Revised: 21 March 2024 Accepted: 22 March 2024 Published: 26 March 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . 1 Department of Mechanical Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA 2 Product Innovation and Engineering LLC, St. James, MO 65559, USA * Correspondence: [liou@mst.edu](liou@mst.edu) 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. Keywords: residual stresses; experimental measurement; computational method; machine learning 1. Introduction

FAQs

Why is predicting residual stresses important in laser additive manufacturing?

Residual stresses can lead to distortion, cracking, and delamination, causing early failure and reduced mechanical performance. Accurate prediction helps prevent part failure and supports product quality.

What are the main approaches covered for predicting residual stresses?

The review addresses predictions using experimental measurement, computational methods, and machine learning. It compares how each approach contributes to modeling and understanding residual stress formation.

What drives residual stress formation during laser additive manufacturing?

Residual stresses arise from uneven cooling rates and thermal gradients during material deposition and solidification. These thermal effects create stress distributions that affect the final part integrity.

Aug 5, 2026

Upload by Hazel