Prediction of levelling process parameters for high-strength plates based on finite element collaborative PSO-BP neural network

This study aims to address the difficulty of straightness control in the straightening of high-strength plates caused by insufficient prediction accuracy of traditional methods. To this end, a high-precision prediction model is developed, and a particle swarm optimization-BP neural network model (PSO-BP) is proposed based on the synergy between finite element simulation and intelligent algorithms. A 3D dynamic explicit finite element model of an 11-roll straightening machine is constructed using ABAQUS to simulate the stress-strain field evolution and flatness change during the entire straightening process of a high-strength plate under various process parameters. Subsequently, 2 000 sets of measured data are collected from the production line, combined with 500 supplementary finite element simulation samples, and a cross-source heterogeneous training dataset is established after standardization. The Particle Swarm Algorithm (PSO) is employed to optimize the initial weights and thresholds of the BP neural network, effectively overcoming the problems of slow convergence, gradient vanishing, and local minima associated with traditional BP neural networks. Experimental validation shows that the PSO-BP model has excellent predictive performance: the correlation coefficient R-value in the training set is 0.957 and in the test set it is 0.965; the Root Mean Square Error (RMSE) has been reduced to 0.027; and the key prediction error rate remains stable at between 3.7% and 8.57%. This is a significant improvement on the traditional Finite Element Method (FEM), which has an error rate ranging from 18.57% to 20%. This study combines PSO global optimization and BP local approximation to achieve a breakthrough in generalization performance under complex process conditions, with prediction results that are highly aligned with actual outcomes. In the future work, the data diversity needs to be expanded to enhance the model′s adaptability and migration capability.