东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2016, Vol. 37 ›› Issue (2): 213-217.DOI: 10.12068/j.issn.1005-3026.2016.02.014

• 机械工程 • 上一篇    下一篇

基于BP和GA的微晶玻璃点磨削表面硬度数值拟合

马廉洁1,2， 巩亚东2， 于爱兵3， 曹小兵1   

  1. (1. 东北大学秦皇岛分校 控制工程学院， 河北 秦皇岛066004; 2. 东北大学 机械工程与自动化学院， 辽宁 沈阳110819; 3. 宁波大学 机械工程与力学学院， 浙江 宁波315211)
  • 收稿日期:2014-12-03 修回日期:2014-12-03 出版日期:2016-02-15 发布日期:2016-02-18
  • 通讯作者: 马廉洁
  • 作者简介:马廉洁(1970-)，男，内蒙古赤峰人，东北大学教授.
  • 基金资助:
    国家自然科学基金资助项目(51275083).

Numerical Fitting of Surface Hardness Based on BP and GA in Point Grinding Low Expansion Glass

MA Lian-jie1,2， GONG Ya-dong2， YU Ai-bing3， CAO Xiao-bing1   

  1. 1.School of Control Engineering， Northeastern University at Qinhuangdao， Qinhuangdao 066004， China; 2.School of Mechanical Engineering & Automation， Northeastern University， Shenyang 110819， China; 3.Faculty of Mechanical Engineering and Mechanics， Ningbo University， Ningbo 315211， China.
  • Received:2014-12-03 Revised:2014-12-03 Online:2016-02-15 Published:2016-02-18
  • Contact: MA Lian-jie
  • About author:-
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摘要： 通过低膨胀微晶玻璃的高速点磨削实验，测试了加工表面硬度，分析了表面硬度随工艺参数的变化趋势.基于BP神经网络算法与单因素实验值，通过最小二乘数值拟合，建立了点磨削低膨胀微晶玻璃表面硬度与各工艺参数关系的系列化一元模型，以决定系数检验模型的精度，结果表明模型具有较高的可靠性.通过单因素一元模型分析，提出了低膨胀微晶玻璃表面硬度与工艺参数关系的多元模型.在正交试验的基础上，基于遗传算法对多元模型进行了优化建模求解.通过验证实验检验了模型的精确度，结果表明，多元模型具有较高的可靠度.

关键词: 表面硬度, 数值拟合, BP神经网络, 遗传算法, 点磨削, 微晶玻璃

Abstract: The changing trend of surface hardness with process parameters was analyzed， and the surface hardness was tested by grinding low expansion glass in quick-point. Based on BP neural network and single factor tests in quick-point grinding， a series of one-dimensional models were built for surface hardness and process parameters by the least-squares fitting. The accuracy of the model was tested by coefficient of correlation. The results show that the model has high accuracy. The multivariate models about surface hardness and process parameters were proposed after analyzing one-dimensional models. Based on the genetic algorithm， the multivariate numerical models were built for surface hardness according to the results of orthogonal experiments. The accuracy of multivariate model was tested by the verification experiment. The test results indicate that the model has high accuracy.

Key words: surface hardness, numerical fitting, BP neural network, genetic algorithm, point grinding, glass ceramics

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