Abstract
The prediction of the combined high and low cycle fatigue (CCF) life of welded components was valuable for the reliability of practical engineering structures. In this study, a physics-informed Grey-deep convolutional neural networks (DCNN) model was constructed and the model parameters was determined based on the fatigue behavior characteristics of CCF, which was proved perform well in the CCF life prediction of welded components. The predicted results indicated that the proposed model can realize reliable CCF life prediction with good accuracy (MAE = 0.49) and stability (RMSE = 0.44). Further, the proposed model also exhibited good prediction performance under different loading and geometry conditions, indicating the good universality of the proposed model. And the good universality represented that the proposed model had the potential to be applied in more engineering fields.
Published Version
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