AccScience Publishing / IJAMD / Volume 2 / Issue 1 / DOI: 10.36922/IJAMD025040004
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ORIGINAL RESEARCH ARTICLE

Improvement of multiaxial fatigue life prediction performance based on contrastive learning feature extraction

Ziyu Cui1 ,  Xingyue Sun2* ,  Xu Chen1,3
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1 Department of Process Equipment & Control Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin, China
2 Department of Aeronautical Structure Engineering, School of Aeronautics, Northwestern Polytechnical University, Xi’an, Shaanxi, China
3 Zhejiang Institute of Tianjin University, Ningbo, Zhejiang, China
IJAMD 2025, 2(1), 54–72; https://doi.org/10.36922/IJAMD025040004
Submitted: 22 January 2025 | Revised: 6 March 2025 | Accepted: 14 March 2025 | Published: 28 March 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Accurate prediction of multiaxial fatigue life was crucial for structural integrity assessment, yet the variability in material responses under complex loading paths made it challenging for both classical and data-driven models to achieve high accuracy. To address this issue, a contrastive learning-based framework was proposed in this study, enabling the construction of more generalized low-dimensional feature representations across different loading paths. This framework enhanced the robustness of fatigue life prediction without relying on mechanical assumptions. Experimental validation demonstrated that, compared to existing methods, the contrastive learning model learned more suitable feature encodings, significantly improving prediction performance. This framework provided a reference solution for engineering applications requiring reliability assessment under multiaxial stress conditions.

Graphical abstract
Keywords
Contrastive learning
Deep learning
Feature engineering
Life prediction
Multiaxial fatigue
Funding
The authors gratefully acknowledge financial support for this work from the National Natural Science Foundation of China (No. 12302098).
Conflict of interest
The authors declared that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
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International Journal of AI for Materials and Design, Electronic ISSN: 3029-2573 Print ISSN: 3041-0746, Published by AccScience Publishing
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