Prediction of springback in local bending of hull plates using an optimized backpropagation neural network

Abstract

Abstract. Springback is an inevitable problem in the local bending process of hull plates, which leads to low processing efficiency and affects the assembly accuracy. Therefore, the prediction of the springback effect, as a result of the local bending of hull plates, bears great significance. This paper proposes a springback prediction model based on a backpropagation neural network (BPNN), considering geometric and process parameters. Genetic algorithm (GA) and improved particle swarm optimization (PSO) algorithms are used to improve the global search capability of BPNN, which tends to fall into local optimal solutions, in order to find the global optimal solution. The result shows that the proposed springback prediction model, based on the BPNN optimized by genetic algorithm, is faster and offers smaller prediction error on the springback due to local bending.

Research on the Curvature Prediction Method of Profile Roll Bending Based on Machine Learning

“…Compared with conventional springback compensation methods, the development cycle was shortened, and the cost and calculation requirements were reduced. Xu et al [16] and Li et al [17] proposed to use the genetic algorithm (GA) and sparrow search algorithm (SSA) to predict the cold bending springback of the hull based on the backpropagation neural network (BPNN), which greatly improved the prediction accuracy and prediction speed. Wasif et al [18] analyzed the influence of various parameters on the bending springback of JSH590 steel by the V-shaped bending test, and applied the genetic algorithm to optimize the process parameters of minimum springback.…”