Abnormality Detection by Analysis of Cutting Sound
This study deals with the construction of network system for the production site, for the purpose of networking the machine tools as a part of the whole system. The Tele-monitoring for transformation of monitored information was studied. From the point of view of monitoring, the recognition of abnormal is very important in reducing the burden of an operator or for long-time unmanned operations. We then propose a method for the detection of abnormal in cutting process by analyzing the cutting sounds using Fourier transform, filtering, correlation analysis and statistic treatment. For the checking of tool wearing progressing, an experiment was arranged for the recognition of the sounds from a worn tip in comparison with a new one. On the detection of tipping of the cutter, a new tip is used for a long-time cutting without tool exchanging. The change of worn width and related values were compared with the original standard. It was found that the detection of tipping is possible by continuously sampling the cutting sounds and monitoring their correlation.
Xing Ai, Jianfeng Li and Chuanzhen Huang
L. Y. Chen et al., "Abnormality Detection by Analysis of Cutting Sound", Materials Science Forum, Vols. 471-472, pp. 172-177, 2004