Discrete wavelet transforms analysis of vibration signals for correlating tool wear in diamond turning of additive manufactured Ti-6Al-4V alloy

Abstract

Ultra-precision machining (UPM) of Ti-6Al-4V alloy is widely regarded as a challenging material processing due to excessive tool wear and chemical reactivity of the tool and workpiece. Tool wear has a significant influence on the surface quality and also causes damage to the substrate. Therefore, it is critical to consider the tool condition during diamond turning, especially as precision machining moves toward intelligent systems. Consequently, there is a need for effective ways for in-process tool wear monitoring in UPM. This study aims to monitor the diamond tool wear using time-frequency-based wavelet analysis on vibrational signals acquired during the machining of Additively Manufactured (AM) Ti6Al4V alloy. The analysis employed Daubechies wavelet (db4, level 8) to establish a correlation between the Standard Deviation (SD) of the magnitude in the decomposed vibrational signal obtained from both the fresh and used tools. The analysis revealed that at a feed rate of 1 mm/min, the change in SD is 32.3% whereas at a feed rate of 5 mm/min, the change in SD is 8.4%. Furthermore, the flank wear and microfractures are observed using a scanning electron microscope on the respective flank and rake face of the diamond tool.