Application Research of Neural Network in the Tensile Constitutive Relationship of Carbon Steel

Most carbon steels are multi-phase alloys with high specific strength, good mid-temperature properties and corrosion resistance, which lead to great differences in mechanical properties. The traditional model has some limitations and only reflects the mechanical behavior of the forming process in a certain range of temperature and strain rate. In this paper, a constitutive model of carbon steel based on neural network is established by using the high precision nonlinear fitting ability and strong generalization ability of neural network, reflect its different stages of mechanical properties. The model is trained, studied and simulated, and the results of the model are compared and analyzed. It is found that the model has high fitting precision and practical application value.