Prediction of Multi-Axis Fatigue Life of Metallic Materials Using a Feature-Optimised Hybrid GRU-Attention-DNN Model

To address the challenge of simultaneously modelling temporal evolution and static properties in fatigue life prediction, this paper proposes a Hybrid GRU–Attention–DNN model: The Gated Recurrent Unit (GRU) captures time-evolution features, while the attention mechanism adaptively focuses on critical stages. These are then fused with static properties via a fully connected network to generate life estimates. Training and validation were conducted using an 8:2 split, with baselines including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and GRU. Performance was evaluated using the coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), and root mean squared logarithmic error (RMSLE), together with error band plots. Results demonstrate that the proposed model outperforms baseline CNN/GRU/LSTM models in overall accuracy and robustness, and that these improvements remain statistically significant according to bootstrap confidence intervals (CI) of R2, RMSE, MAE and RMSLE on the test set. Additionally, this paper conducts an interpretability analysis: attention visualisations reveal the model’s significant emphasis on the early stages of the lifespan. Time window masking experiments further indicate that removing early information causes the most significant performance degradation. Both lines of evidence show high consistency in qualitative and quantitative trends, providing a basis for engineering sampling window design and trade-offs in test duration.