A vision-based approach for surface roughness assessment at micro and nano scales

This paper presents a vision-based approach for valid assessment of surface roughness in both micro-scale and nano-scale regions. To enable data comparisons, three sets of surface data in the micro and nano regions are acquired by using a CCD camera, a video-based optical microscope and a stylus instrument. Data filtering and analysis procedures are applied to the acquired data. Results for computation of roughness parameters by using vision data provide adequate values for assessment of surface roughness in the manner as similar as stylus based technique. No obvious changes in the computed roughness parameter values are resulted from the micro and nano regions. In the nano region, a cavity graphs technique provides distinguishable forms of graphs that tend to more gradual increase of the cavity percentage to denote the collection of the macro surface details. In addition, an auto correlation technique applied in the nano region succeeds to discriminate the surface irregularities relationship with respect to their periodicity and randomness. The overall acquired results indicate that vision systems are a valid source of data for reliable surface roughness evaluation in both micro/nano-scale regions. The results are very useful in achieving commercial 3D vision based micro-nano roughness measurement systems for industrial applications.