Flank wear prediction in tungsten carbide tool using acoustic signal amplitude in conventional turning operation. (October 2021)

• Record Type: Journal Article
• Authors: Sreeraj, PR; Elias, Jiju V; Manu, R; Mathew, Jose
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 235:Number 19(2021)
• Page Start: 4070
• Page End: 4083
• Publication Date: 2021-10
• Subjects: Acoustic emission -- signal processing -- fast Fourier transform -- machining -- flank wear; Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1177/0954406220969726
• Languages: English
• ISSNs: 0954-4062

Abstract

A tool wear prediction system that senses the cutting tool wear and allows for the optimum utilization of the tool is a deciding factor in achieving good surface quality of machined products. In recent years, the use of acoustic emission signals that emerged during machining operations has gained great significance in accurately predicting the tool condition. The objective of this work is to determine the variation in acoustic signal characteristics with tool flank wear in conventional turning process using Fast Fourier Transformation (FFT). Unlike the previous works, the experiments in this work were performed after optimizing the distance of the sensor position from the tip of the tool, since the optimum position of the sensor determines the transmission of the acoustic signals with minimum attenuation. The obtained raw signal output of the acoustic sensor is processed using FFT and the spectral analysis of the signal is carried out to determine the frequencies and also the magnitude of signal content at each frequency. This study establishes a correlation between the flank wear and the amplitude of acoustic signals. The model is validated using three sets of experiments at various cutting conditions. The average percentage deviation of the predicted tool wear from the experimental results is found to be 9.97%. This study indicates that the AE signals can be used to model the flank wear progression and can be useful for the accurate flank wear prediction in machining processes.