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

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.