基于BP和GA的微晶玻璃点磨削表面硬度数值拟合  

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

作　　者：马廉洁[1,2] 巩亚东[2] 于爱兵[3] 曹小兵[1] 

机构地区：[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

出　　处：《东北大学学报（自然科学版）》2016年第2期213-217,共5页Journal of Northeastern University(Natural Science)

基　　金：国家自然科学基金资助项目(51275083)

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.The changing trend of surface hardness with process parameters was analyzed,and the surface hardness was tested by grinding lowexpansion 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 showthat 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.

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 

分 类 号：TH161[机械工程—机械制造及自动化]