Milling dataset

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 a comprehensive dataset for the full lifecycle of titanium (Ti6Al4V) tool wear. This dataset 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 and 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 this dataset will be a crucial resource for smart manufacturing research.

深度学习方法在刀具磨损全生命周期分析领域已展现出显著应用潜力。然而，由于数据采集与设备时间投入成本高昂，开源数据集较为匮乏。现有数据集往往无法直接捕捉切削力的动态变化。本文提出一套覆盖钛合金（Ti6Al4V）刀具磨损全生命周期的综合数据集。该数据集采用复杂的圆周铣削路径，并使用旋转式测力仪（rotary dynamometer）直接测量切削力与扭矩，同时收录从初始磨损到严重磨损全阶段的多维度数据。数据集包含68组不同样本，每组样本约含500万条数据记录，涵盖振动信号、声学信号、切削力与扭矩四类数据。此外还提供了详细的刀具磨损形貌图像与对应测量值。本数据集可用于时间序列预测、异常检测及刀具磨损相关研究，对于智能制造研究领域具有重要价值，我们相信该数据集将成为智能制造研究的关键资源。