中国机械工程Issue(2)：217-222,6.DOI:10.3969/j.issn.1004132X.2015.02.016
LS-SVM 回归算法在刀具磨损量预测中的应用
Application of Regression Algorithm of LS-SVM in Tool Wear Prediction
摘要
Abstract
Aiming at online predicting tool wear accurately,a method based on the regression algo-rithm of LS-SVM was proposed.First the acoustic emission signals were decomposed into several in-trinsic mode functions(IMF)employing empirical mode decomposition.Then,an AR model of each IMF was established respectively.AR model coefficients were extracted to construct feature vector.Fi-nally,the feature vectors were feed into LS-SVM and prediction of tool wear was realized.The experi-mental results show that it can predict the amount of tool wear after 10s according to the current cut-ting conditions and the proposed method has better accuracy compared with neural network algo-rithm.关键词
刀具磨损量预测/最小二乘支持向量机/经验模态分解/自回归模型Key words
tool wear prediction/lease square support vector machine(LS-SVM)/empirical mode decomposition(EMD)/auto regressive(AR)model分类
机械制造引用本文复制引用
关山,闫丽红,彭昶..LS-SVM 回归算法在刀具磨损量预测中的应用[J].中国机械工程,2015,(2):217-222,6.基金项目
东北电力大学博士科研启动基金资助项目(BSJXM-201115) （BSJXM-201115）
