【摘要】 对于金属切削过程中的刀具磨损,提出了基于隐马尔可夫模型的模式识别理论来识别刀具的不同磨损状态,从而预报刀具破损。该方法对切削过程中切削力信号的动态分量和刀柄振动信号进行快速傅里叶变换特征提取,然后利用自组织特征映射对提取的特征矢量进行预分类编码,把矢量编码作为观测序列引入到隐马尔可夫模型中进行机器学习,建立了3个不同磨损状态的隐马尔可夫模型,并利用最大概率进行模式识别。试验表明,该方法对车刀磨损过程进行识别和预报是有效的。更多还原

【Abstract】 A method of pattern recognition based on discrete hidden Markov model(DHMM) is proposed to monitor tool wears and predict tool failures in the cutting process.At first the FFT features are extracted from the signal of the tollholder vibration and the cutting force in cutting process,then the FFT vectors are presorted and coded into code book of integer numbers by using the self-organizing feature map(SOM),and these code books are introduced to DHMM for machine learning to build up 3-HMMs for different tool wear stages.And then,pattern of hidden Markov model(HMM) is recognized by using maximum probability.Finally,the results of tool wear recognition and failure prediction experiments show that the method is effective.更多还原