SDU-QIT立铣刀磨损试验数据集

SDU-QIT End Milling Cutter Accelerated Life Test Datasets

刀具故障预测与健康管理(Prognostic and health management,PHM)是机床制造领域的关键问题。作为数控机床的“牙齿”,刀具的健康状态直接影响着机床加工效率和产品质量。借助大数据与人工智能(AI)技术实现对刀具运行状态的实时精准监测,目前已成为学术界和工业界关注的热点问题。然而,刀具高质量全寿命周期数据的匮乏,严重制约了机械装备PHM技术的理论研究与工程应用。为解决上述问题,开展了面向刀具全寿命周期的数控加工中心立铣刀多工况试验与数据采集工作,并将获取的试验数据集面向全球学者公开发布。该数据集共包含2种工况下的立铣刀全寿命周期振动信号,且明确标注了刀具主后刀面最大VB值、主后刀面1/2ap(背吃刀量)处VB值、主后刀面SVB值,副后刀面最大VB值、副后刀面SVB值等5种标签,可为PHM领域基于AI的刀具故障诊断与预测性维护研究提供数据支撑。

The Prognostic Health Monitoring(PHM)of milling cutters is key issue in the field of machine tool manufacturing.As the"teeth"of computer numerical control(CNC)machine tools,its health status directly affects the machining efficiency and the quality of products.It has become a hot topic in both academic research and industries,with the help of big data and deep learning technology to realize high reliable tool fault diagnosis and predictive maintenance.However,the lack of high-quality yet life-cycle data has become the bottleneck restricting academic research and engineering application.To solve this problem,the end milling cutter life test in CNC machining center are carried out,and the obtained test data set are published for scholars all over the world.This data set contains the vibration signals of the end milling cutter in the whole life cycle under two working conditions,and clearly marks five groups of data,such as the maximum wear width at the main back of the cutter,wear area SVB and wear width VB at 1/2ap(back feed),and the maximum wear width and wear area SVB at the auxiliary back,which provides data support for the research in PHM field.