Classification of the acoustic emission signals of rubbing, ploughing and cutting during single grit scratch tests

Abstract

The material process in grinding involves rubbing, ploughing and cutting. Rubbing and ploughing, which usually occur before or after cutting, essentially mean the energy is being applied less efficiently than cutting. It is therefore important to identify the effects of these different phenomena experienced during grinding. A fundamental investigation has been made with Single Grit (SG) cutting tests. To identify the different phenomena, Acoustic Emission (AE) signals extracted by sensor would give the information relating to the groove profile in terms of material removal and deformation. Each AE signal profile would be accurately measured against the surface profile measurements providing the dimensions of the indented groove made into the aerospace material test pieces. A combination of filters, Short-Time Fourier Transform (STFT), Wavelets Transform (WT), statistical windowing of the WT would provide the principal components for classifying different levels of SG cutting phenomenon. Classification would be obtained from a Neural Network (NN) paradigm.