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，由于数据采集成本高昂且设备耗时投入较大，当前开源刀具磨损数据集较为稀缺。现有数据集往往无法直接捕捉切削力的变化情况。本文提出了一套面向钛合金（Ti6Al4V）刀具磨损全生命周期的综合性数据集。该数据集采用复杂圆周铣削路径，并使用旋转式测力仪直接采集切削力与扭矩数据，同时涵盖从初始磨损到严重磨损全阶段的多维度传感数据。本数据集共包含68组独立样本，单组样本数据量约达500万行，涵盖振动、声音、切削力及扭矩四类传感数据，同时提供了详细的刀具磨损图像与测量值。该数据集可作为时间序列预测、异常检测及刀具磨损研究领域的宝贵资源。我们相信本数据集将为智能制造相关研究提供关键支撑资源。