Springback prediction model and its compensation method for the variable curvature metal tube bending forming

2021 ◽  
Vol 112 (11-12) ◽  
pp. 3151-3165
Author(s):  
Shuyou Zhang ◽  
Mengyu Fu ◽  
Zili Wang ◽  
Dingyu Fang ◽  
Weiming Lin ◽  
...  
Keyword(s):  
Prediction Model ◽  
Compensation Method ◽  
Metal Tube ◽  
Tube Bending ◽  
Variable Curvature ◽  
Springback Prediction

scholarly journals Springback prediction of thick-walled high-strength titanium tube bending

2013 ◽  
Vol 26 (5) ◽  
pp. 1336-1345 ◽  
Author(s):  
Feifei Song ◽  
He Yang ◽  
Heng Li ◽  
Mei Zhan ◽  
Guangjun Li
Keyword(s):  
High Strength ◽  
Tube Bending ◽  
Titanium Tube ◽  
Springback Prediction

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

2021 ◽  
Vol 12 (2) ◽  
pp. 777-789
Author(s):  
Binjiang Xu ◽  
Lei Li ◽  
Zhao Wang ◽  
Honggen Zhou ◽  
Di Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Prediction Model ◽  
Optimal Solution ◽  
Backpropagation Neural Network ◽  
Processing Efficiency ◽  
Model Based ◽  
Assembly Accuracy ◽  
Bending Process ◽  
Springback Prediction

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.

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