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TOOL WEAR MONITORING IN MACHINING PROCESSES THROUGH WAVELET ANALYSIS

机译:小波分析在加工过程中的刀具磨损监测

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摘要

A new approach for tool wear monitoring in machining processes based on wavelet transform is presented. The discrete wavelet transformation (DWT) is used to extract feature vectors from vibration signals measured during turning. The feature vectors form an observation sequence, which is a sequence of vectorized parameters consisting of average energy at each scale of the wavelet decomposition. Vibration signals in the feed direction are used for detailed analysis. A series of experiments were then conducted to evaluate the effectiveness of the wavelet-based approach. The feature vectors for sharp and worn tools gave distinct patterns, which were classified using a 3-state hidden Markov model. Experimental results showed the effectiveness of the approach in detecting a sharp and a worn tool.
机译:提出了一种基于小波变换的加工过程中刀具磨损监测的新方法。离散小波变换(DWT)用于从车削过程中测得的振动信号中提取特征向量。特征向量形成观察序列,该观察序列是由小波分解的每个尺度上的平均能量组成的向量化参数序列。进给方向的振动信号用于详细分析。然后进行了一系列实验,以评估基于小波方法的有效性。锋利且磨损的工具的特征向量给出了不同的模式,这些模式使用三态隐藏马尔可夫模型进行了分类。实验结果表明,该方法在检测锋利和磨损的工具方面是有效的。

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