Elastic-plastic modeling method based on temporal convolutional network

Because the deformation of the material under load is strongly correlated with the loading path, the Temporal Convolutional Network (TCN), which can satisfy the time-series learning, is chosen to model the elastic-plastic intrinsic relationship of the material. The ABAQUS software is used to write scripts to construct the data set required for training. After training and learning the stress-strain states and their increments for different loading states, the constructed network model can well characterize the yielding process and the subsequent yielding evolution of the material. Compared with the traditional phenomenological model, it has better flexibility and computational efficiency, and provides a new method for constructing the constitutive model of materials.