High Precision and Rapid Springback Prediction for Creep Forming of Ribbed Panel Based on SPSO-BP Neural Network

doi:10.3901/JME.2025.20.081

Abstract

Abstract: Creep age forming can realize the shape-properties integrated manufacturing of large and complex thin-walled components. However, the determination of key parameters such as process system and die surface requires complicated finite element calculation iterations, which leads to long development cycle and high cost. Based on SPSO-BP neural network, a high precision and rapid springback prediction method for creep age forming of ribbed panel is proposed. By extracting key modeling parameters of forming process and characterizing discrete points of structure features of ribbed panel, a BP neural network model for predicting the relationship between material properties, ageing process, panel structure, die surface and component’s springback surface was established. Furthermore, the SPSO particle swarm optimization considering chaotic Logistic mapping function was used to optimize the initial weight and threshold of the BP neural springback model, and the accurate training and data set expansion of the neural network springback model were realized. The experimental results show that the RMSE error of SPSO-BP prediction model is only 0.68 mm and the calculation efficiency is 90 times higher than that of conventional finite element simulation. The research results can be used to rapidly locate the processing parameters of creep age forming for large components and shorten the development cycle significantly.

Key words: creep age forming, springback prediction, particle swarm optimization, BP neural network, ribbed panel

CLC Number:

TG156

ZHAN Lihua, ZHAO Shuai, YANG Youliang, CHEN Zanchong, XIE Hao, LIU Changzhi, YAN Dongyang. High Precision and Rapid Springback Prediction for Creep Forming of Ribbed Panel Based on SPSO-BP Neural Network[J]. Journal of Mechanical Engineering, 2025, 61(20): 81-90.

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