Correlation between Roughness (Ra) and Fractal Dimension (D) Using Artificial Vision Systems for On-Site Inspection

Abstract

A set of images of metal pieces with different finishes was obtained for use as a standard reference in the estimate of the surface roughness (Ra) through the fractal dimension (D) of the power spectrum and the spectrum of intensities. A system of artificial vision with two sources of LED light illumination (white and red) and three angles of incidence was used. The best results were found with the spectra of intensities regardless of the type of lighting or the angle of incidence. The values of the fractal dimension were correlated with the surface roughness values obtained with a contact profilometer to build regression curves that are used to estimate the surface roughness on site with a statistical error under 5%. This system could be used as an inspection station to reduce waiting times and unnecessary transport (Poka Yoke System).

Relationship analysis of surface roughness measurements on coatings using AFM and fractal dimension by mesoscopic model methods

“…Structured surface, complex time series, and difficulty describing dividing lines are much more common than can be expected [32]. Considering the previous investigation into the influence of performance roughness and the dimension of the fractal measurement on coating [11]. The performance roughness is factor A, and the two interest levels are n=1 and multilayer.…”

“…information on the relationship between roughness and fractal dimension surface on a specimen of steel out coating using a system of artificial vision with two sources of light-emitting diode (LED) light illumination. This study correlated the fractal dimension with a contact profilometer's surface roughness values [11]. Nowadays, surface engineering studies focus on understanding the strong relationship between fractal analysis using the traditional method and optical-statistical processing assistance for modern science and technology [12], even the optical-statistical can propose a standardized and excellent potential methodology to evaluate surface roughness.…”