謝秀嫻，付攀，曹偉青 （西南交通大學，四川 成都 610031）   摘要：隨著現代加工工業的發展，對刀具磨損的監測在保障生產安全和產品質量中發揮著越來越重要的作用。聲發射技術是刀具磨損監測的一種新方法。在車削加工過程中采集聲發射信號，用聲發射信號對刀具磨損狀態進行識別。利用小波包分解技術對信號進行分析，得到有效的特征量作為BP神經網絡的輸入樣本，并對網絡進行學習訓練，完成對刀具磨損狀態的有效識別。   關鍵詞：刀具磨損；聲發射；小波包分析；神經網絡   中圖分類號：TP206＋.1 文獻標識碼：A 文章編號：1672-4984(2006)02-0040-03   Acoustic emission and wavelet analysis-based estimation of tool wear   XIE Xiu-xian,FU Pan,CAO Wei-qing (Southwest Jiaotong University, Chengdu 610031，China)   Abstract：Accompanied with the development of modern machining industry, tool wear monitoring becomes more and more important. Acoustic Emission (AE) is a useful and effective technique in tool wear monitoring. This paper uses Daubechies Wavelet to analyze AE signal and select features of the tools. The selected features are then considered as inputs to BP neural network to complete recognition of the status of the cutting tool.   Key words：Tool wear; Acoustic emission; Wavelet analysis; Neural network