高嵩

个人信息Personal Information

副教授

博士生导师

硕士生导师

教师拼音名称:gaosong

出生日期:1987-04-08

电子邮箱:

入职时间:2015-09-01

所在单位:机电工程学院

职务:机械工程系主任

学历:博士研究生毕业

办公地点:机电工程学院1649

性别:男

学位:工学博士学位

在职信息:在职

毕业院校:大连理工大学

学科:机械制造及其自动化
车辆工程

论文成果

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Springback Prediction and Optimization of Variable Stretch Force Trajectory in Three-dimensional Stretch Bending Process

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所属单位:长春工业大学机电工程学院

教研室:机械工程系

发表刊物:Chinese Journal of Mechanical Engineering

刊物所在地:中国-北京

项目来源:National Technical Innovation Foundation of China (No. Jilin Province 350)

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

摘要: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.

合写作者:张万喜,高嵩

第一作者:滕菲,梁继才

通讯作者:梁继才,张万喜,高嵩

学科门类:工学

一级学科:机械工程

文献类型:Journal

卷号:28

期号:6

页面范围:1132-1140

ISSN号:1000-9345

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发表时间:2015-11-01