摘要
为实现微细电火花孔加工的电极形状损耗形式的分类预测,选择SVM支持向量机、BP神经网络、Logistic回归、KNN临近算法四种典型的分类算法,分别建立了微细电极损耗形式的分类模型,并根据分类情况,结合理论分析,探讨了形状控制的一般方法,同时设计了验证实验。结果表明:Logistic回归模型最为合适,能较好贴合实验数据;在小的脉冲能量下选取大的脉冲宽度有利于实现电极的均匀损耗,且此种方法具有一定的通用性。研究成果初步实现了微细电极的形状预测与控制,对提高微细电火花加工精度具有一定的指导意义。
In order to realize the classification and prediction of electrode shape wear form in micro-EDM drilling,four typical classification algorithms,Support Vector Machine(SVM),BP neural network,Logistic regression and K-Nearest Neighbor,are selected to establish the classification model of micro electrode wear form.According to the classification and theoretical analysis,the general method of shape control is discussed,and the verification experiment is designed.The results show that the Logistic regression model is the most suitable,which can fit the experimental data well,and the selection of large pulse width under small pulse energy is beneficial to the realization of electrode even wear,and this method has a certain universality.The research results preliminarily realize the shape prediction and control of micro electrode,which has certain guiding significance for improving the accuracy of micro-EDM.
作者
王慧
王元刚
李晓鹏
WANG Hui;WANG Yuangang;LI Xiaopeng(School of Mechanical Engineering,Dalian University,Dalian 116622,China)
出处
《电加工与模具》
2020年第5期10-13,共4页
Electromachining & Mould
基金
国家自然科学基金资助项目(51005027)。
关键词
微细电火花孔加工
形状损耗
分类模型
控形方法
micro-EDM drilling
shape wear of electrode
classification model
shape control method
