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兵工学报 ›› 2010, Vol. 31 ›› Issue (8): 1125-1128.

• 研究简报 • 上一篇    下一篇

冲压式气动系统的建模与线性化分析

辛民1,2, 解丽静1, 王西彬1, 石文天1, 杨洪建1   

  1. (1.北京理工大学 机械与车辆学院, 北京 100081;2.中国神华铁路货车运输分公司, 北京 100011)
  • 收稿日期:2009-01-04 修回日期:2009-01-04 上线日期:2014-05-04
  • 通讯作者: 辛民 E-mail:glorious97@163.com
  • 作者简介:辛民(1979—)男, 博士研究生
  • 基金资助:
    国防预先研究项目(513181604)

Modeling and Linearity Analysis of Ram-air Servo System

XIN Min1,2, XIE Li-jing1, WANG Xi-bin1, SHI Wen-tian1, YANG Hong-jian1   

  1. (1.School of Mechanical and Vehicular Engineering, Beijing Institute of Technology, Beijing 100081, China;2.Railway Vehicle Transportation Branch,China Shenhua Group Corporation Limited, Beijing 100011, China)
  • Received:2009-01-04 Revised:2009-01-04 Online:2014-05-04
  • Contact: XIN Min E-mail:glorious97@163.com

摘要: 针对零件铣削加工变形难以在线检测的难点,提出了一种基于神经网络的加工变形在线预测方法,通过正交试验方法设计试验方案,进行了不同铣削参数条件下的铣削试验,以试验数据为训练样本建立了基于BP神经网络的铣削加工变形与铣削参数关系的预测模型。通过生产试验验证,此模型在样本参数覆盖范围内的模型精度可达99.56%、在覆盖范围外的模型精度高于95.47%,说明该神经模型能定量的反映出铣削参数与加工变形之间的关系。

关键词: 机械制造工艺与设备, 变形, BP神经网络, 铣削参数, 预测模型

Abstract: In order to settle the troubles in on-line deformation measurement of workpiece, a deformation prediction method based on artificial neutral network was proposed. The cutting experiment scheme was designed by using orthogonal experiment technique. The experiments in different milling parameters were carried out, the workpieces' deformations were measured, and the deformation prediction model was created by using a Back-Propagation neural network and taking the experiment data as its training examples. According to the validation of the additional production experiments, the model's accuracy in the scope of examples is 99.56% and better than 95.47% outside the scope of examples. The results show that the deformation prediction model can accurately reflect the relationship between deformation and milling parameters.

Key words: machinofacture technique and equipment, deformation, BP neural network, milling parameter, prediction model

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