Machined Surface Image Analysis for Process Instability Identification in Micromilling of Ti6Al4V

Abstract

The low flexural rigidity of micro-end mill used in micromilling process undergoes catastrophic failure at low amplitude chatter and hence it is critical to identify the low amplitude chatter to produce the chatter free machined surfaces. An unfixing of the workpiece to identify the chatter by analyzing the power spectral density of machined surface topography alters the process set-up and even a minute change in workpiece surface flatness during re-fixing the workpiece lead to variation in depth of cut, which changes the precision of machined surface in micromilling process. Consequently, in the present work, an image analysis method has been proposed for machined surface analysis in micromilling of Ti6Al4V. Different statistical parameters like arithmetic gray level average value of the distribution of pixel intensities and optical parameter have been analyzed to detect the process instability. The estimated statistical parameters of the machined surface images for different process parameters has been compared with the measured surface roughness of the machined surfaces. The frequency spectrum analysis of the machined surface image analysis has also been carried out, which shows the distribution of pixel intensities at different frequencies. Arithmetic gray level average of pixel intensity of the machined surface image has been found to be decreasing with an increases of the roughness of the machined surface. The optical parameter of the machined surface increases with the increases of the roughness. The critical value of arithmetic gray level average of pixel intensity and optical parameter was found to be 148 and 0.36, respectively for chatter onset. The distribution of pixel intensity at different frequencies shows the diffused pattern of brightness of the pixel away from the center location in chatter dominant machining while the distribution of brightness of pixel was found to be circular in nature around the central spot in stable machining. The chatter and stable machining condition has also been verified with the vibration spectrum of the workpiece, where the vibration of the workpiece has been captured using accelerometer. The proposed image analysis method can further be used for online monitoring of machining process.