Optimization of Hot Stamping Parameters for Aluminum Alloy Crash Beams Using Neural Networks and Genetic Algorithms

The hot stamping process of aluminum alloys involves multiple parameters, including blank holder force, stamping speed, die temperature, and friction coefficient. Traditional methods often fail to capture the nonlinear interactions among these parameters. This study proposes an optimization framework that integrates BP neural networks with genetic algorithms (GA), while six bio-inspired algorithms—Grey Wolf Optimization (GWO), Sparrow Search Algorithm (SSA), Crested Porcupine Optimizer (CPO), Grey lag Goose Optimization (GOOSE), Dung Beetle Optimizer (DBO), and Parrot Optimizer (PO)—were employed to optimize the network hyperparameters. Comparative results show that all optimized models outperformed the baseline BP model (R2 = 0.702, RMSE = 0.106, MAPE = 20.8%). The PO-BP achieved the best performance, raising R2 by 27.3% and reducing MAPE by 27.1%. Furthermore, combining GA with the PO-BP model yielded optimized process parameters, reducing the maximum thinning rate to 17.0% with only a 1.16% error compared with experiments. Overall, the proposed framework significantly improves prediction accuracy and forming quality, offering an efficient solution for rapid process optimization in intelligent manufacturing of aluminum alloy automotive parts.