Common methods of in-process surface roughness measurement are capable of providing a limited number of amplitude parameters for roughness assessment. This is mainly due to the averaging effect of the sensors used. In this work, we measured the amplitude, spacing, hybrid as well as functional surface roughness parameters during dry turning of AISI 1035 carbon steel using machine vision. A commercial DSLR camera with high shutter speed was used to capture a blur-free image of the workpiece surface profile diametrically opposite the cutting tool. The edge of the surface profile was detected to sub-pixel accuracy using the grey level invariant moment and the roughness parameters were determined from the profile. The tool nose wear and machining time were correlated with amplitude, hybrid, and spacing surface roughness parameters, as well as the bearing area curve parameters. Three new roughness parameters, namely average slope of profile peaks (Φp), average slope of profile valleys (Φv), relative length of peaks (Rrl), were introduced to study the effect of changes in tool nose micro geometry due to wear on the surface roughness. Among these new parameters Φp and Rrl showed better correlation with machining time and nose wear compared to all other parameters.
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