A neural network-based prediction method of machining deformation for thin-walled workpiece

被引：0

作者：

机构：

[1] Key Laboratory of Nondestructive Testing of Ministry of Education, Nanchang Hangkong University, Nanchang 330063, Jiangxi

来源：

Qin, G.-H. (qghwzx@126.com) | 2013年 / China Ordnance Society, P.O. Box 2431, Beijing, 100081, China卷 / 34期

关键词：

BP neural network; Finite element analysis; Mechanical manufacturing process and equipment; Prediction; Tool structure; Workpiece deformation;

D O I：

10.3969/j.issn.1000-1093.2013.07.007

中图分类号：

学科分类号：

摘要：

During the cutting operation of thin-walled workpiece, the tool parameter is an important factor causing the workpiece deformation. The deformation law of workpiece which is caused by the single tool angle can be obtained by finite element method. However, if multiple tool angles are synchronously considered only by using finite element method, the deformation law of workpiece is difficult to reveal. Therefore, the 3D finite element analysis model is established for the milling process of thin-walled workpiece. The comparison of the simulated values with the experimental results is carried out to validate the proposed finite element model. Thus, the viable finite element method can be used to obtain the training samples of neural network. And then, with the nonlinear mapping of neural network, the prediction model of workpiece deformation is suggested according to the finite training samples. Finally, the relative error in less than 3% of the predicted deformation to the corresponding simulated result shows that the proposed prediction model can be used to correctly obtain the workpiece deformation.

引用

页码：840 / 845

页数：5

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