Vibration-based tool wear monitoring using Artificial Neural Networks fed by Spectral centroid indicator & RMS of CEEMDAN modes

Abstract In machining processes various phenomena occur during cutting operation. These phenomena can disturb the production through the reduction of part quality and accuracy. Therefore, a mastery of this cutting phenomena is needed to define the machining parameters and take full advantage of manufacturing process. An easy way to classify these phenomena is by monitoring incontrollable parameters, such as generated temperature and vibration. The acquired vibration signals can provide information regarding tool life, 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 qualified the SCI indicator and ANN for online monitoring.