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We present an efficient algorithm for monitoring milling tool wear using data from a spindle integrated Cutting Force Sensor. In a near-real-world milling experiment, we collected quality data of the manufactured part, tool wear, and cutting forces. A preprocessing pipeline transforms the data from time-domain to angle-domain and ensures its integrity. Using Singular Value Decomposition (SVD) for dimensionality reduction, we achieve a compact encoding of key sensor data and avoid manual intervention in the feature engineering. Notably, the SVD reconstruction error proves to be a reliable indicator for tool wear.

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Towards Real-Time Tool Wear Detection on Edge Devices: A Lightweight Dimensionality Reduction Approach for Spindle Integrated Cutting Force Sensor Data

  • 会议论文

  • 发表日期：

    2026年

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  • Sebastian Unsin,
  • Benedikt Müller,
  • Thomas Jäkel,
  • Frank Schirmeier

摘要

We present an efficient algorithm for monitoring milling tool wear using data from a spindle integrated Cutting Force Sensor. In a near-real-world milling experiment, we collected quality data of the manufactured part, tool wear, and cutting forces. A preprocessing pipeline transforms the data from time-domain to angle-domain and ensures its integrity. Using Singular Value Decomposition (SVD) for dimensionality reduction, we achieve a compact encoding of key sensor data and avoid manual intervention in the feature engineering. Notably, the SVD reconstruction error proves to be a reliable indicator for tool wear.