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

Abstract

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.