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Affiliation of Author(s):长春工业大学机电工程学院

Teaching and Research Group:机械工程系

Journal:Chinese Journal of Mechanical Engineering

Place of Publication:中国-北京

Funded by:National Technical Innovation Foundation of China (No. Jilin Province 350)

Key Words:springback prediction; support vector regression (SVR); response surface; particle swarm optimization (PSO)

Abstract:Most of the existing studies use constant force to reduce springback while researching stretch force. However, variable stretch force can reduce springback more efficiently. The current research on springback prediction in stretch bending forming mainly focuses on artificial neural networks combined with the finite element simulation. There is a lack of springback prediction by support vector regression (SVR). In this paper, SVR is applied to predict springback in the three-dimensional stretch bending forming process, and variable stretch force trajectory is optimized. Six parameters of variable stretch force trajectory are chosen as the input parameters of the SVR model. Sixty experiments generated by design of experiments (DOE) are carried out to train and test the SVR model. The experimental results confirm that the accuracy of the SVR model is higher than that of artificial neural networks. Based on this model, an optimization algorithm of variable stretch force trajectory using particle swarm optimization (PSO) is proposed. The springback amount is used as the objective function. Changes of local thickness are applied as the criterion of forming constraints. The objection and constraints are formulated by response surface models. The precision of response surface models is examined. Six different stretch force trajectories are employed to certify springback reduction in the optimum stretch force trajectory, which can efficiently reduce springback. This research proposes a new method of springback prediction using SVR and optimizes variable stretch force trajectory to reduce springback.

Co-author:张万喜,gaosong

First Author:滕菲,梁继才

Correspondence Author:梁继才,张万喜,gaosong

Discipline:Engineering

First-Level Discipline:Mechanical Engineering

Document Type:Journal

Volume:28

Issue:6

Page Number:1132-1140

ISSN No.:1000-9345

Translation or Not:no

Date of Publication:2015-11-01