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

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.

Keywords

Contrastive learning / Deep learning / Feature engineering / Life prediction / Multiaxial fatigue

Acknowledgments

None.

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.

Author contributions

Conceptualization: Xingyue Sun

Formal analysis: Ziyu Cui

Investigation: Xingyue Sun, Ziyu Cui

Methodology: Xingyue Sun

Writing - original draft: Ziyu Cui

Writing - review & editing: Xingyue Sun, Xu Chen

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data

The data are available from the corresponding author upon reasonable request.

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