Articles | Volume 12, issue 2
https://doi.org/10.5194/ms-12-777-2021
https://doi.org/10.5194/ms-12-777-2021
Research article
 | 
09 Aug 2021
Research article |  | 09 Aug 2021

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

Binjiang Xu, Lei Li, Zhao Wang, Honggen Zhou, and Di Liu

Related subject area

Subject: Machining and Manufacturing Processes | Techniques and Approaches: Mathematical Modeling and Analysis
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Cited articles

Dib, M. A., Oliveira, N. J., Marques, A. E., Oliveira, M. C., Fernandes, J. V., Ribeiro, B. M., and Prates, P. A.: Single and ensemble classifiers for defect prediction in sheet metal forming under variability, Neural. Comput. Appl., 32, 12335–12349, https://doi.org/10.1007/s00521-019-04651-6, 2020. 
Froitzheim, P., Stoltmann, M., Fuchs, N., Woernle, C., and Flugge, W.: Prediction of metal sheet forming based on a geometrical model approach, Int. J. Mater. Form., 13, 829–839, https://doi.org/10.1007/s12289-019-01529-9, 2019. 
Guo, Z. F. and Tang, W. C.: Bending Angle Prediction Model Based on BPNN-Spline in Air Bending Springback Process, Math. Probl. Eng., 2017, 1–11, https://doi.org/10.1155/2017/7834621, 2017. 
Hamouche, E. and Loukaides, E. G.: Classification and selection of sheet forming processes with machine learning, Int. J. Comput. Integ. M., 31, 921–932, https://doi.org/10.1080/0951192X.2018.1429668, 2018. 
Hou, Y., Min, J., Lin, J., Liu, Z., and Stoughton, T. B.: Springback prediction of sheet metals using improved material models, Procedia Eng., 207, 173–178, https://doi.org/10.1016/j.proeng.2017.10.757, 2017. 
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Short summary
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. In total, four springback prediction models, based on genetic and back propagation neural network (GA-BPNN) algorithms and the improved particle swarm optimization (PSO)-BPNN algorithms, are established.