Investigations on the effects of tool wear on chip formation mechanism and chip morphology using acoustic emission signal in the microendmilling of aluminum alloy

Abstract

This work investigates the effects of tool wear on surface roughness (Ra), chip formation mechanisms and chip morphology in the microendmilling of aluminum alloy (AA 1100) using acoustic emission (AE) signals. The acquired AE signals are analysed in the time domain, frequency domain using fast Fourier transformation (FFT) and the discrete wavelet transformation (DWT) technique. The time domain analysis indicates that the root mean square of the AE (AERMS) signals is sensitive to the formation of the buildup edge apart from effective machining. The frequency domain analysis indicates that the dominant frequency of the AE signals lies between 150 and 300 kHz. The AE-specific energies are computed by decomposing the AE signals in different frequency bands, using the DWT technique. The higher and lower orders of AE-specific energies are obtained. The higher order of AE-specific energies indicates chip formation mechanisms such as shearing and microfracture. Chip morphology studies are carried out using the FFT analysis. The FFT indicates that low-frequency and low-amplitude AE lead to tight curl chips, while high-frequency and high-amplitude AE lead to elemental/short comma chips. This work provides new significant inferences on tool wear, chip formation mechanisms and chip morphology in the microendmilling of AA 1100.