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

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.

Wear analysis in cutting tools by the technique of image processing with the application of two-dimensional matrices

“…The next step was to isolate the pixels containing the wear mark information, in this case the selected level shown in Figure 4(a), in yellow is extracted, and stored in a black and white file Figure 4(b), with which the process is continued. It can be seen that in addition to the wear mark there are other areas that have the selected brightness level but that must be removed since they do not correspond to the area of interest, to achieve part of that target was used a morphological processing with "bwmorph", which allows to remove gaps, points and stains from the image [23][24][25], the result can be seen in Figure 4(c), although the process can improve the image to the point where the wear mark is clearly distinguished, it can contain large areas that must be removed.…”

Study on In-Situ Tool Wear Detection during Micro End Milling Based on Machine Vision

“…Zhuang et al [ 6 ] fused vibration signals, cutting temperature signals and cutting force signals to propose a tool wear status monitoring method based on the data-driven approach. Prakash et al [ 7 ] used acoustic emission signals to identify the wear state of micro milling tools to investigate the relationship between acoustic emission signals and the chip formation mechanism. This type of indirect measurement method based on cutting signal acquisition and processing enables monitoring the condition of the tool during cutting in addition to predicting the tool wear status.…”