Regime-Switched Neural Networks: Flow Stress Modeling Strategy of 310s Stainless Steel During Hot Deformation

This study examines the high temperature deformation and constitutive modeling of flow stress for 310s stainless steel. To this end, hot tensile experiments were conducted under the temperatures of 700° and 800° at the strain rates of 0.0002/s, 0.002/s, and 0.02/s. Flow stress was modeled using the Arrhenius type constitutive equation and neural network approach. Specifically, Regime-Switched Neural Networks (RSNN), a set of neural networks with switching, has been newly proposed as a better predictive model for flow stress. The modeling performance of the RSNN model was evaluated through a comparison with traditional Arrhenius-type constitutive equations and the single neural network model. The results showed that the accuracy of the proposed RSNN was substantially higher than those of the existing Arrhenius-type equations, and the prediction performance was therefore significantly improved. In addition, the accuracy of the proposed RSNN was improved by approximately more than 24% in comparison with the existing global model—a single neural network—thus confirming the superiority of the proposed switching model.