摘要
以微径铣刀磨损程度的识别为研究对象,考虑可能出现的单齿切削现象,建立了刀具磨损的隐Mark-ov模型。模型首先判断刀具在稳态切削情况下是否出现单齿切削现象,随后以小波分解的方式分别提取切削力特征。通过Fisher线性判别提取8个最优的切削力特征,作为隐Markov模型训练的输入向量。对于多组切削参数为单齿切削和两齿交替切削,分别训练三个不同磨损阶段的隐Markov模型,用以识别刀具真实磨损状态,并通过Euclidian线性判别确定最适应的识别模型。实验结果表明,该方法能够准确识别微径铣刀磨损状态,准确率在85%左右。
By taking the micro-milling tool wear identification as research object and through considering the possible phenomenon of single edge cutting,Hidden Markov Model（HMM） of tool wear was established.HMM judged whether the single edge cutting phenomenon appeared or not in steady-state cutting condition firstly.Then wavelet packet decomposition was used to extract the cutting force feature.Eight optimal cutting force features were extracted as HMM training input vectors by Fisher linear discriminance.For single edge cutting and two edges alternative cutting of multiple cutting parameters,three different wear stage HMMs were established to identify the actual wear state of tools,and the most suitable recongnition model was determined through Euclidian linear discriminance.The experimental results showed that the micro-milling tools wear state could be accurately identified by HMM,and the accuracy rate was about 85%.
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2012年第1期141-148,共8页
Computer Integrated Manufacturing Systems
基金
国家科技重大专项资助项目(2009ZX04002-051)~~
