Thermo-Mechanical Simulation and ANN-Based Prediction of Rolling Force and Torque in Two-Layer Copper–Aluminum Composite Panel

In this study, a hybrid modeling framework combining three-dimensional finite element (FE) simulation and artificial neural network (ANN) prediction is developed to analyze the asymmetric hot rolling behavior of a two-layer Cu/AA2030 composite panel. The FE model, implemented using the Abaqus/Explicit platform, couples thermal and mechanical fields to evaluate the effects of key process parameters including initial panel thickness, reduction ratio, rolling speed, and inlet temperature on rolling force and torque. The asymmetric configuration of the composite panel and the thermal-mechanical interaction between the rolls and panel are fully considered in the simulation. A validated FE dataset was subsequently employed to train a feed-forward back-propagation ANN using the Levenberg–Marquardt algorithm. The network architecture, consisting of four input neurons, two hidden layers, and two output neurons, was optimized to achieve minimum mean square error (MSE) and high correlation accuracy between predicted and simulated values. Results indicate that both rolling force and torque increase with greater thickness reduction and initial panel thickness, while higher rolling temperatures and rolling speeds reduce the required force and torque. The ANN model successfully predicts rolling force and torque with high accuracy, demonstrating strong generalization and computational efficiency. This integrated FE–ANN approach provides a reliable and time-effective method for optimizing process parameters in bimetallic panel rolling, reducing the need for extensive experimental trials and enabling improved control of rolling performance in copper–aluminum laminated composites.