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<p>Deep learning methods have shown significant potential in tool wear lifecycle analysis. However, there are fewer open source datasets due to the high cost of data collection and equipment time investment. Existing datasets often fail to capture cutting force changes directly. This paper introduces QIT-CEMC, a comprehensive dataset for the full lifecycle of titanium (Ti6Al4V) tool wear. QIT-CEMC utilizes complex circumferential milling paths and employs a rotary dynamometer to directly measure cutting force and torque, alongside multidimensional data from initial wear to severe wear. The dataset consists of 68 different samples with approximately 5 million rows each, includes vibration, sound, cutting force and torque. Detailed wear pictures and measurement values are also provided. It is a valuable resource for time series prediction, anomaly detection, and tool wear studies. We believe QIT-CEMC will be a crucial resource for smart manufacturing research.</p>

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A multi-feature dataset of coated end milling cutter tool wear whole life cycle

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月6日

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  • Na Li,
  • Xiao Wang,
  • Wanzhen Wang,
  • Miaomiao Xin,
  • Dongfeng Yuan,
  • Mingqiang Zhang

摘要

Deep learning methods have shown significant potential in tool wear lifecycle analysis. However, there are fewer open source datasets due to the high cost of data collection and equipment time investment. Existing datasets often fail to capture cutting force changes directly. This paper introduces QIT-CEMC, a comprehensive dataset for the full lifecycle of titanium (Ti6Al4V) tool wear. QIT-CEMC utilizes complex circumferential milling paths and employs a rotary dynamometer to directly measure cutting force and torque, alongside multidimensional data from initial wear to severe wear. The dataset consists of 68 different samples with approximately 5 million rows each, includes vibration, sound, cutting force and torque. Detailed wear pictures and measurement values are also provided. It is a valuable resource for time series prediction, anomaly detection, and tool wear studies. We believe QIT-CEMC will be a crucial resource for smart manufacturing research.