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<p>Anomaly detection in CNC milling machine operation is a necessary component to develop a smart, autonomous CNC milling machine. These anomalies range from phenomena such as chatter, to tool breakage, to work piece misalignment. There have been many methods for detecting different anomalies, but many of these methods are focused on classifying one anomaly at a time. In this work, we propose an LSTM autoencoder-based system to apply dimensionality reduction on accelerometer signals and then apply neighborhood components analysis (NCA) for multi-class classification and prediction. We show that this approach is capable of detecting the onset of cutting with a broken tool in three axis CNC machines and differentiate it from stable and chatter cutting conditions.</p>

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Multi-class anomaly classification for three axis CNC milling machines

  • 期刊论文

  • 发表日期：

    2025年8月15日

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  • Eugene Li,
  • Parasmai Conjeevaram,
  • Sanjeev Bedi,
  • William Melek

摘要

Anomaly detection in CNC milling machine operation is a necessary component to develop a smart, autonomous CNC milling machine. These anomalies range from phenomena such as chatter, to tool breakage, to work piece misalignment. There have been many methods for detecting different anomalies, but many of these methods are focused on classifying one anomaly at a time. In this work, we propose an LSTM autoencoder-based system to apply dimensionality reduction on accelerometer signals and then apply neighborhood components analysis (NCA) for multi-class classification and prediction. We show that this approach is capable of detecting the onset of cutting with a broken tool in three axis CNC machines and differentiate it from stable and chatter cutting conditions.