In this research, hot deformation experiments of 316L stainless steel were carried out at a temperature range of 800–1000 °C and strain rate of 2 × 10⁻³–2 × 10⁻¹. The flow stress behavior of 316L stainless steel was found to be highly dependent on the strain rate and temperature. After the experimental study, the flow stress was modeled using the Arrhenius-type constitutive equation, a neural network approach, and the support vector regression algorithm. The present research mainly focused on a comparative study of three algorithms for modeling the characteristics of hot deformation. The results indicated that the neural network approach and the support vector regression algorithm could be used to model the flow stress better than the approach of the Arrhenius-type equation. The modeling efficiency of the support vector regression algorithm was also found to be more efficient than the algorithm for neural networks.

In this study, the flow stress of Ti-6Al-4V during hot deformation was modeled using a decision tree algorithm. Hot compression experiments for Ti-6Al-4V in a Gleeble-3500 thermomechanical simulator were performed under a strain rate of 0.002–20 s^–1 and temperatures of 575–725 °C. After the experiments, flow stress behavior was modeled, first by a traditional Arrhenius type equation, second by utilizing the artificial neural network, and lastly, with the aid of the decision tree algorithm. While the characteristics of measured flow stress were noticeably dependent on the resulting strain rate and temperature, the modeling accuracy regarding the flow stress results of the Arrhenius type equation, neural network approach and decision tree algorithm were compared. The decision tree algorithm predicted the flow stress most effectively.

The alloy 304 stainless steel is used in a wide variety of industrial applications. It is frequently applied in tough environments, such as those involving high temperatures, low temperatures, and corrosive environments. Hence, research on the flow stress behavior of the alloy during deformation under tough environments is critically important to achieving the maximum effectiveness in the application of the alloy. This research presents a study on the flow stress of 304 stainless steel during hot deformation at the temperatures of 700 °C–900 °C under the strain rates ranging from 0.0002/s–0.02/s. For this study, hot tensile experiments are conducted, and the flow stress variations of the alloy are studied with respect to the variations in the strain rate and temperature. Next, the stress behavior was modeled by the traditional Arrhenius-type constitutive equation and random forest algorithm. Then, the flow stresses predicted by different methods were studied by comparing errors. The results showed that the flow stress was modeled more accurately by the random forest algorithm.

This study presents the adoption of locally constrained regression models (LCRMs) with logarithmic transformations in order to model the flow stress behavior of the high-temperature deformation of 5005 aluminum alloy. Hot tensile tests for 5005 aluminum alloy were conducted under the temperatures of 290 °C, 360 °C, 430 °C, and 500 °C, and the strain rates of 0.0003/s, 0.003/s, and 0.03/s. The flow stress behavior was analyzed based on variations in temperature and strain rate. The flow stress during the hot deformation was modeled using the traditional Arrhenius type constitutive equation and the neural network approach. Then, for improved prediction accuracy, the flow stress was modeled using LCRMs. The prediction accuracies of the models were compared by calculating the MAE (Maximum Absolute Error) and RMSE (Root-Mean-Squared Errors) values. The MAE and RMSE of the LCRMs were lower than the errors of the Arrhenius equation and the neural network model. The results show that LCRMs can be useful in modeling the flow stress of 5005 aluminum alloy, and that the developed model can accurately predict the flow stress.

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°C and 800°C 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.