Purpose -This paper aims to propose a non-contact method using machine vision for measuring the surface roughness of a rotating workpiece at speeds of up to 4,000 rpm. Design/methodology/approach -A commercial digital single-lens-reflex camera with high shutter speed and backlight was used to capture a silhouette of the rotating workpiece profile. The roughness profile was extracted at sub-pixel accuracy from the captured images using the moment invariant method of edge detection. The average (Ra), root-mean square (Rq) and peak-to-valley (Rt) roughness parameters were measured for ten different specimens at spindle speeds of up to 4,000 rpm. The roughness values measured using the proposed machine vision system were verified using the stylus profilometer. Findings -The roughness values measured using the proposed method show high correlation (up to 0.997 for Ra) with those determined using the profilometer. The mean differences in Ra, Rq and Rt between the two methods were only 4.66, 3.29 and 3.70 per cent, respectively. Practical implications -The proposed method has significant potential for application in the in-process roughness measurement and tool condition monitoring from workpiece profile signature during turning, thus, obviating the need to stop the machine. Originality/value -The machine vision method combined with sub-pixel edge detection has not been applied to measure the roughness of a rotating workpiece.
In the past, most researchers have investigated the effect of tool flank wear on the average roughness parameter (Ra) during turning. The Ra
parameter, however, is sensitive only to the height variations of the machined workiece profile and is insensitive to the spatial changes to the profile caused by gradual increase in tool wear. In this work, a non-contact vision-based method was used to investigate the effect of tool nose radius wear on the hybrid surface roughness parameters during finish turning. The roughness profile of the workpiece surface diametrically opposite the cutting tool was captured during turning of AISI 1035 steel using a digital camera. A high shutter speed (1/4000 s) was used to freeze the motion of the rotating spindle and obtain a blur-free image of the workpiece. The edge of the workpiece profile was extracted to sub-pixel accuracy using the grey level invariant moment method. The hybrid surface roughness parameters were determined from the workpiece profile. The effect on increased nose wear area on these parameters were investigated. Among these roughness parameters the mean wavelength (Rλa) parameter showed the best correlation with tool nose radius wear with coefficients of determination of 0.9734.
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