北京航空航天大学学报 ›› 2016, Vol. 42 ›› Issue (8): 1691-1697.doi: 10.13700/j.bh.1001-5965.2015.0493

• 论文 • 上一篇    下一篇

基于机器学习的管材数控弯曲质量预测

葛宇龙, 李晓星, 郎利辉, 程鹏志   

  1. 北京航空航天大学 机械工程及自动化学院, 北京 100083
  • 收稿日期:2015-07-22 出版日期:2016-08-20 发布日期:2015-12-08
  • 通讯作者: 李晓星,Tel.:010-82338968,E-mail:li.xiaoxing@buaa.edu.cn E-mail:li.xiaoxing@buaa.edu.cn
  • 作者简介:葛宇龙,男,博士研究生。主要研究方向:数字化板料成形技术。E-mail:AaronGe@buaa.edu.cn;李晓星,男,博士,教授,博士生导师。主要研究方向:数字化板料成形技术。Tel.:010-82338968。E-mail:li.xiaoxing@buaa.edu.cn

Tube numerical controlled bending quality prediction based on machine learning

GE Yulong, LI Xiaoxing, LANG Lihui, CHENG Pengzhi   

  1. School of Mechanical Engineering and Automation, Beijing University of Aeronautics and Astronautics, Beijing 100083, China
  • Received:2015-07-22 Online:2016-08-20 Published:2015-12-08

摘要: 在管材数控(NC)弯曲过程中,可能出现起皱、过度减薄的质量缺陷,同时会不可避免地发生回弹,都将严重影响成形质量。为了对数控弯曲成形质量进行预测,提出了使用有限元模拟与机器学习相结合的方法,并建立了快速的成形质量预测方法。首先,建立了有效的管材数控弯曲的参数化有限元模型,在工艺参数取值范围中随机选择进行大量的模拟实验作为样本,完成学习数据的挖掘。随后,基于径向基函数(RBF)神经网络建立壁厚减薄与回弹程度的预测模型并使用支持向量机(SVM)建立管材起皱的预测模型。最后,使用模型对新的实例进行预测,并利用模拟与数控弯曲实验对预测模型进行验证。 该方法可以对大直径薄壁管材数控弯曲质量进行有效的预测,提高弯曲管件零件设计效率。

关键词: 管材数控(NC)弯曲, 起皱, 回弹, 壁厚减薄, 径向基神经网络, 支持向量机(SVM)

Abstract: Wrinkle and over thinning, as well as inevitable spring-back, may occur along with the tube numerical controlled (NC) bending process, which have strong impacts on forming quality. As the bending processing is a complex non-linear system, it is hard to compute the result theoretically. Besides, finite element simulation is a time-consuming method for industry.To predict the forming quality of the NC bending, a rapid method based on the machine learning method and finite element modeling is raised. To apply the method, the first step is to build the finite element model of tube bending and make simulations whose process parameters are selected randomly as samples.After extracting experimental data, a radius basis function (RBF) neural network and a support vector machine (SVM) are built to predict thinning, spring-back and wrinkle separately.New instances are taken to verify the prediction method. The results show that the machine learning method can reliably predict the large diameter thin-walled tube NC bending quality and improve the efficiency of part forming process design.

Key words: tube numerical controlled (NC) bending, wrinkle, spring-back, wall thinning, radial basis neural network, support vector machine (SVM)

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