Vibration-based tool wear monitoring using artificial neural networks fed by spectral centroid indicator and RMS of CEEMDAN modes

In machining processes, various phenomena occur during cutting operation. These phenomena can disturb the production through the reduction of part quality and accuracy. An easy way to control the process is by monitoring incontrollable parameters, such as generated temperature and vibration. The acquired vibration signals can provide information regarding tool life, surface roughness, cutting performances, and workpiece defects. This paper evaluates the possibility of monitoring the tool life during the turning process of AISI 1045 steel using laser Doppler vibrometer (LDV); the surface roughness has been measured along with the tool wear until reaching its limit value of 300μm. Furthermore, this paper also outlines the application of CEEMDAN technique to process the acquired signals for the monitoring processes. RMS and SCI indicators have been used to describe the wear progress, then, the artificial neural network has been adopted to achieve a real-time wear monitoring. The obtained results show that the CEEMDAN helps for isolating tool vibration signature. The RMS indicator does not provide enough information about the wear behavior; however, good results have been achieved by SCI indicator. The ANNs fed by SCI deliver accurate results allowing for real-time wear monitoring.