Li Na, Wang Xiao, Wang Wanzhen, Xin Miaomiao, Yuan Dongfeng, Zhang Mingqiang
School of Intelligent Manufacturing and Control Engineering, Qilu Institute of Technology, Jinan, 250200, China.
Shandong Provincal Key Laboratory of Industrial Big Data and Intelligent Manufacturing, Qilu Institute of Technology, Jinan, 250200, China.
Sci Data. 2025 Jan 6;12(1):16. doi: 10.1038/s41597-024-04345-2.
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.
深度学习方法在刀具磨损生命周期分析中已显示出巨大潜力。然而，由于数据收集成本高和设备时间投入大，开源数据集较少。现有数据集往往无法直接捕捉切削力变化。本文介绍了QIT-CEMC，这是一个用于钛（Ti6Al4V）刀具磨损全生命周期的综合数据集。QIT-CEMC采用复杂的圆周铣削路径，并使用旋转测力计直接测量切削力和扭矩，以及从初始磨损到严重磨损的多维数据。该数据集由68个不同样本组成，每个样本约有500万行，包括振动、声音、切削力和扭矩。还提供了详细的磨损图片和测量值。它是时间序列预测、异常检测和刀具磨损研究的宝贵资源。我们相信QIT-CEMC将成为智能制造研究的关键资源。
