LAUSR

Tool wear drives poor surface quality, dimensional error in the workpiece, and unexpected sudden tool failure during the machining process. Thus, detection of tool wear is essential to increase the workpiece quality and extend tool life. In this view, acoustic emission technique (AET) was employed to investigate the tool wear characteristics for different tool geometries during drilling of Al-5%SiC metal matrix composite (MMC). The dry drilling experiments were performed for different cutting speeds and feed rates ranging from 600-1200 rpm and 0.07–0.17 mm/rev, respectively. The high strength steel (HSS) tool with different point angles (90°, 118°, and 135°) was used for the drilling tests. The captured acoustic emission (AE) signals were analyzed in time domain, frequency domain, and time-frequency domain. AE count, energy, peak amplitude, and root mean square voltage (AE RMS ) were correlated with cutting parameters (spindle speed and feed rate). The Fast Fourier Transform (FFT) and Continuous Wavelet Transform (CWT) of AE signals could identify the predominant peak frequency and time-frequency spectrum. The relationship between tool wear and AE parameter (wavelet coefficient) for different tool geometries was studied. Wavelet packet transform (WPT) was utilized to extract various AE source features present in the signals. The WPT results could distinguish different frequency components and the related damage mechanisms involved in the drilling process. The damage in the cutting tool and drilled workpiece were also characterized using a scanning electron microscope (SEM).