SDU-QIT End Milling Cutter Accelerated Life Test Datasets
The Prognostic/ Predictive Health Monitoring (PHM) and Fault diagnosis of milling cutters are key issue in the field of machine tool manufacturing. As the "teeth" of CNC machine tools, their health status directly affects the machining efficiency and the quality of products. It has become a hot topic in both academic research and industries, which propose to employ 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 promotion.
Supported by the Major Scientific and Technological Innovation Project of Shandong Province "R&D and application demonstration of key technologies for new adaptive production systems in the field of machine tool manufacturing", the research team of Shandong University cooperated with Qilu Institute of Technology to jointly setup a multi-sensor based data colloection platform on the machining tools.
At present, the datasets is complete run-to-fault data of 6 end mill.Datasets is open, and it is verified that the tool prediction algorithm of CNC machining tools can be used by anyone.
The datasets of SDU-QIT end milling cutter accelerated life can be downloaded by one of the following links:
⇓ Baidu Netdisk:
https://pan.baidu.com/s/1BmKaKhedU7QKB3t9TzThOw Extraction code：rw97
https://faculty.sdu.edu.cn/zhouxiaotian1/en/zhym/1333305/list/index.htm
If you have any questions or suggestions, do not hesitate to contact:
Prof. Dongfeng Yuan, dfyuan@sdu.edu.cn