Springback prediction for incremental sheet forming based on FEM-PSONN technology
(1. College of Mechanical and Electrical Engineering, North China University of Technology, Beijing 100144, China;
2. State Key Laboratory of Material Processing and Die and Mould Technology,Huazhong University of Science and Technology, Wuhan 430074, China)
2. State Key Laboratory of Material Processing and Die and Mould Technology,Huazhong University of Science and Technology, Wuhan 430074, China)
Abstract: In the incremental sheet forming (ISF) process, springback is a very important factor that affects the quality of parts. Predicting and controlling springback accurately is essential for the design of the toolpath for ISF. A three-dimensional elasto-plastic finite element model (FEM) was developed to simulate the process and the simulated results were compared with those from the experiment. The springback angle was found to be in accordance with the experimental result, proving the FEM to be effective. A coupled artificial neural networks (ANN) and finite element method technique was developed to simulate and predict springback responses to changes in the processing parameters. A particle swarm optimization (PSO) algorithm was used to optimize the weights and thresholds of the neural network model. The neural network was trained using available FEM simulation data. The results showed that a more accurate prediction of springback can be acquired using the FEM-PSONN model.
Key words: incremental sheet forming (ISF); springback prediction; finite element method (FEM); artificial neural network (ANN); particle swarm optimization (PSO) algorithm