组合机床与自动化加工技术
組閤機床與自動化加工技術
조합궤상여자동화가공기술
MODULAR MACHINE TOOL & AUTOMATIC MANUFACTURING TECHNIQUE
2014年
4期
89-93
,共5页
刀具磨损%支持向量机%神经网络%决策融合
刀具磨損%支持嚮量機%神經網絡%決策融閤
도구마손%지지향량궤%신경망락%결책융합
tool wear%support vector machine( SVM)%neural network%decision fusion
针对常用的贝叶斯算法和D-S证据论的局限性提出了基于支持向量机( SVM)的决策融合方法。建立了能够实时监测车削加工过程中振动和声发射信号的刀具磨损状态监测系统，在对分析信号进行BP和Elman神经网络识别的基础上，利用支持向量机实现了决策融合。实验结果证明，基于支持向量机的决策融合方法具有良好的识别率和鲁棒性，且比单用某一种网络节省时间，更有利于实现切削加工刀具状态的在线监测。
針對常用的貝葉斯算法和D-S證據論的跼限性提齣瞭基于支持嚮量機( SVM)的決策融閤方法。建立瞭能夠實時鑑測車削加工過程中振動和聲髮射信號的刀具磨損狀態鑑測繫統，在對分析信號進行BP和Elman神經網絡識彆的基礎上，利用支持嚮量機實現瞭決策融閤。實驗結果證明，基于支持嚮量機的決策融閤方法具有良好的識彆率和魯棒性，且比單用某一種網絡節省時間，更有利于實現切削加工刀具狀態的在線鑑測。
침대상용적패협사산법화D-S증거론적국한성제출료기우지지향량궤( SVM)적결책융합방법。건립료능구실시감측차삭가공과정중진동화성발사신호적도구마손상태감측계통，재대분석신호진행BP화Elman신경망락식별적기출상，이용지지향량궤실현료결책융합。실험결과증명，기우지지향량궤적결책융합방법구유량호적식별솔화로봉성，차비단용모일충망락절성시간，경유리우실현절삭가공도구상태적재선감측。
Decision fusion method based on support vector machine is proposed for the limitations of com-monly used Bayesian algorithms and D-S evidence theory. Tool wear condition monitoring system capable of real-time monitoring signal vibration and acoustic emission signals in the turning process was established. The Decision fusion is achieved using support vector machine, based on BP and Elman neural network rec-ognition signal. Experimental results show that decision fusion method based on support vector machine has a good recognition rate and robustness. At the same time, this approach saves time than single neural net-work, online monitoring of the cutting tool wear state is more easy to implement.
