Prediction of plastic yield surface for porous materials by a machine learning approach

The present paper focuses on the prediction of effective plastic yield surface of porous materials having a von Mises type solid matrix. Some typical explicit yield criteria obtained by different analytical homogenization methods are briefly reviewed and evaluated by using numerical results obtained from direct finite element simulations with different values of porosity. Each criterion has its own advantage and weakness. In order to get a better prediction, the Artificial Neuron Network (ANN) algorithm is adopted specially for the prediction of macroscopic yield stress of porous materials, seen as a regression problem with two input parameters and one output value. For the training purpose which is a key step in the ANN approach, new numerical results are presented in the present work with a wide range of porosity and of macroscopic stress triaxiality. Based on these data, the ANN approach is trained and it converges quickly. Then the ANN predictions are compared with numerical test data, a good agreement is found for all loading cases. Comparing with the existing yield criteria, the prediction given by the ANN approach is much more accuracy and easy to apply.