Data-Driven Construction Method of Material Mechanical Behavior Model

To obtain the mechanical behavior response of the material under loading, a data-driven construction method of material mechanical behavior model is proposed, which is universal for predicting the mechanical behavior of any material under different loads. Based on the framework of artificial intelligence and finite element simulation, the method uses Python script to drive an Abaqus loop calculation to obtain data sets and performs artificial intelligence training on data sets to realize model construction. In this paper, taking the quasi-static tension of 9310 steel as an example, a material mechanical behavior model is constructed, and the accuracy of the prediction model is verified based on the experimental data. The results show that the simulation results are in good agreement with the experimental data. The error between the simulation results and the experimental results is within 2%, indicating that the model constructed by this method can effectively predict the mechanical properties of materials.

Automated summary

This paper proposes a data-driven method for constructing a material mechanical behavior model, which can predict the mechanical behavior of any material under different loads. The accuracy of the prediction model is verified based on experimental data.