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 minutes 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, the signals has then been decomposed with ICEEMDAN, the energy of the high frequency modes has been finally calculated. The Spectral Kurtosis has allowed the locating of the optimal frequency band that contains the machining vibration signature, yet it couldn't have been used for wear monitoring. On the other hand, the energy of optimal frequency ICEEMDAN modes has increased in direct proportion to the increase of surface roughness degradation and thus, to tool wear.

Effect of Wear on Vibration Amplitude and Chip Shape Characteristics during Machining of Eco-Friendly and Leaded Brass Alloys

“…Chuangwen [15] obtained results for 022Cr17Ni12Mo2 stainless steel where the root-meansquare value of vibration acceleration signals increased with wear progression in general. Bouhalais [16] confirmed a good match between high-frequency vibration components' energy values and tool wear monitoring. Sridhar [17] found ten times larger vibration amplitudes of a worn drill compared to a sharp one.…”

Tool Wear Prediction Based on Residual Connection and Temporal Networks

“…For the direct method, the tool wear is directly measured with an optical microscope or a CCD camera based on computer vision methods [7]. In the indirect method, models of TCM are established based on signals collected by sensors, such as cutting force [8], vibration [9], acoustic emission [10], and spindle power [11]. Compared with direct methods that require machine tools to be shut down for measuring, indirect methods allow in situ estimates of tool condition based on sensor data.…”

A Multi-scale Hybrid Wind Power Prediction Model Based on LSSVR-RELM-Multi-Head Attention-BiGRU and Data Decomposition

“…Addressing residual noise and pseudo-mode concerns further, Colominas et al [31] introduced the Improved Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), demonstrating heightened reconstruction accuracy for components and enhanced suitability for nonlinear signal analysis. For instance, Mohaned L B et al [32] proposed a tool wear detection method based on spectral decomposition and ICEEMDAN mode energy, with experimental findings showcasing enhanced detection precision using this decomposition method.…”