Prediction of Hot Rolling Force for Aluminum Alloys Driven by Data and Mechanism

Aluminum alloy hot rolling features diverse varieties, large variations in incoming strip thickness, and strong process nonlinearity. Traditional rolling force prediction models rely on simplified physical assumptions and poor adaptability, making it hard to satisfy high-precision production requirements. This paper presents a mechanism–data dual-driven PSO-BP neural network method for rolling force prediction which is applicable to the rolling temperature range of 320 °C to 520 °C. The SIMS mechanism model is employed as a physical constraint, and a hybrid PSO-GD algorithm optimizes the initial weights and thresholds of the BP network, avoiding the local optimum issue of conventional BP. The rolling mechanism model is embedded into the loss function to deeply integrate physical laws and data-driven learning. Validation using 508 sets of field data from 5083 aluminum alloy hot rolling shows that the model achieves a MAPE of 5.0794% and R2 of 0.9254, significantly outperforming the traditional mechanism model (8.91%) and standard BP (8.77%). The proposed model preserves physical interpretability while utilizing data-driven adaptability, offering an effective approach for high-precision rolling force prediction and improving the dimensional accuracy of hot-rolled aluminum alloy sheets.