A dual-channel convolutional neural network with attention mechanism DC_EcaNet-6 for creep life prediction of notched components

Machine learning models offer novel possibilities for creep life prediction of materials and components at elevated temperatures. Present studies primarily focus on material-level creep life prediction, with limited reports on component-level analysis due to the complex stress states around structural discontinuities. Based on this, a dual-channel convolutional neural network with attention mechanism, DC_EcaNet-6, is proposed for creep life prediction of notched components, where the images of Mises stress and stress tri-axiality are employed as the input. The prediction results by the DC_EcaNet-6 model are compared with that by some deep learning models and the simplified method. Creep reliability assessment of notched components using the DC_EcaNet-6 model is conducted. The results indicate that the proposed model provides a more superior creep life prediction accuracy of notched components than other models mentioned. This model provides a potential tool for creep life prediction and reliability assessment of notched components.