中国机械工程2016，Vol.27Issue(6)：761-766,6.DOI:10.3969/j.issn.1004-132X.2016.06.010
基于PSO算法改进BP神经网络的氟金云母点磨削工艺参数优化
Process Parameter Optimization Based on PSO-BP Neural Network in Point Grinding Fluorophlogopite
摘要
Abstract
Through a high speed point grinding experiment,the surface hardness and roughness of the finished surface was tested,and the variations of the surface hardness and roughness with the process parameters were analyzed.Single factor experimental values were predicted with PSO-BP.A se-ries of one-dimensional models of surface hardness and roughness process parameters of fluorophlogo-pite were built by least-squares fitting.Correlation coefficient test was used to verify the models'high reliability.Multivariate models about surface hardness and roughness process parameters were pro-posed by analyzing one-dimensional models.The multivariate numerical models were optimized accord-ing to the results of orthogonal experiments and PSO and were proved to have high reliability by ex-periment.A dual obj ective optimization of two multivariate models was carried out by PSO algorithm, and a set of reasonable process parameters was obtained.关键词
工艺参数/PSO算法/BP神经网络/点磨削/氟金云母Key words
process parameter/particle swarm optimization(PSO)algorithm/BP neural network/point grinding/fluorophlogopite分类
机械制造引用本文复制引用
马廉洁,陈杰,巩亚东,王佳..基于PSO算法改进BP神经网络的氟金云母点磨削工艺参数优化[J].中国机械工程,2016,27(6):761-766,6.基金项目
国家自然科学基金资助项目(51275083) （51275083）
