Acoustic emission signals analysis to differentiate the damage mechanism in the drilling of Al-5%B4C metal matrix composite

Abstract

Tool wear leads to dimensional inaccuracy and low surface quality in the workpiece, and unexpected sudden tool failure. Detection of tool wear is essential to enhance the quality of manufacturing components and extend tool life. The present work is aimed to investigate the various damage mechanisms involved in the cutting tool and workpiece during drilling of Al-5%B4C composite using acoustic emission technique (AET). The dry drilling experiments were carried out at different spindle speeds and feed rates with high strength steel (HSS) tool. AE time-domain parameters such as count, energy, amplitude and root mean square (RMS) voltage were extracted from the signals and correlated with cutting parameters and tool damage. Fast Fourier transform (FFT) was applied to visualize the frequency components in the AE signals during the drilling process. The wavelet packet transform (WPT) approach was performed to the AE signals to identify and discriminate the various damage mechanism involved in the drilling. The differentiated damage mechanism and their corresponding wavelet energy content were studied. The wavelet energy ratio for decomposed components at different speeds was discussed. The vision measuring microscope was employed to measure the tool wear. The AE features, i.e., AERMS and wavelet coefficient increases with increasing tool wear. A scanning electron microscope was also utilized to characterize the microstructural damage present in the cutting tool and workpiece.