The analysis of tool vibration signals by spectral kurtosis and ICEEMDAN modes energy for insert wear monitoring in turning operation

Abstract

Surface finish quality is becoming even more critical in modern manufacturing industry. In machining processes, surface roughness is directly linked to the cutting tool condition; a worn tool generally produces low-quality surfaces, incurring additional costs in material and time. Therefore, tool wear monitoring is critical for a cost-effective production line. In this paper, the feasibility of a vibration-based approach for tool wear monitoring has been checked for turning process. AISI 1045 unalloyed carbon steel has been machined with TNMG carbide insert twenty-one times for a total of 27 min of machining, which was a necessary amount of time to exceed (300 μm) as a flank wear threshold. Vibration signals have been acquired during the operation and then processed in order to extract a correlation between the surface roughness, tool wear level, and vibration comportment. First, spectral kurtosis has been calculated for the twenty-one performed runs signals; this step has allowed the locating of the optimal frequency band that contains the machining vibration signature, yet it did not give significant information about wear evolution. The signals have then been decomposed with ICEEMDAN and the energy of the high-frequency modes has been calculated. It has been found that the energy of the optimal frequency ICEEMDAN modes has increased in proportion to the increase of surface roughness degradation and thus, to tool wear increase. Therefore, IMF’s energy can be used for tool wear condition monitoring.