Identification of tool wear status and correlation of chip morphology in micro-end milling of mild steel (SAE 1017) using acoustic emission signal

Abstract

Abstract This study describes the identification of micro-end mill wear by means of acoustic emission (AE) signals received from an AE sensor during the micro-end milling (slot milling) of mild steel. The obtained AE signals were processed in the time-domain to compute root mean square (RMS) and dominant frequency and amplitude are obtained from frequency-domain. The RMS value shows the linear trend with the tool wear, and helps to classify the tool wear regions, such as initial, progressive and accelerated wear regions. The Welch power spectral density and spectrogram (short term Fourier transform) analysis help to identify the tool rotational, tool passing and machining frequencies. The discrete wavelet transformation (DWT) technique is used to discretize the AE signal in to five frequency ranges. AE specific energy was obtained from the discretized AE signals. The AE specific energy indicated that a combined type of material removal mechanism occurred in micro-end milling, similar to the macro-end milling. However, ploughing is also observed from the surface topography. Chip structures are also studied and correlated with the micro-end mill wear for tool wear identification.