Vibration Analysis for Real-Time Tool Wear Detection in Turning Processes

In today’s manufacturing industry, there is a growing demand for high-quality, precision-machined parts. However, one of the biggest challenges facing manufacturers is tool wear. As tools wear progress, they become less effective at cutting which can lead to poor surface finish, dimensional accuracy, and even tool failure. Vibration analysis is a non-destructive method that can be used to detect tool wear in real-time. Signal monitoring and analysis of the vibration characteristics on the cutting tool during the cutting process, it is possible to identify changes in the cutting conditions that are indicative of tool wear. Thus, the corrective action before the tool failure leads to preventing costly production delays and ensuring that high-quality parts are produced. This paper presents tool wear detection in real-time using the vibration analysis method. A vibration sensor is installed on the CNC turning center for detecting vibration data. A set of cutting experiments was conducted and vibration signals were then collected. The tool wear analysis was employed on observing changes in Power Spectral Density (PSD) of the vibration signals obtained by the Fast Fourier Transform (FFT) method. The remarkable results presented that the amplitude of the PSD increases as the tool wear increases. This study demonstrates that vibration analysis is a reliable and effective method for detecting tool wear in turning processes. This method can be used to improve the quality and efficiency of manufacturing processes, leading to significant cost savings for manufacturers.