In this work we used the profilogramms of surfaces, which were gained after the surface was processed (coarsened) with abrasive paper of 24, 36, 40, 60 and 100 numbers. We assessed fractal dimensions of all profilogramms and founded dependence of fractal dimension on abrasive paper number. We found that the more is the coarse of the surface the smaller is fractal dimension. As we didn’t want to do experiment which ask many resources – we modeled theoretically researched profilogramms–fractals, calculated and modeled dimensions of fractal profilogramms. We created programmable tools for theoretical research: the profilogramms-fractals models of adequate surfaces, also examined fractal dimensions of modeled profilogramms. With every profilogramm which has adequate fractal dimension we modeled the surface and calculated his area. We applied the linear regression model for logarithmic data and founded the interrelation between the area or the surface and fractal dimension.

The profilogramms are interpreted as realization of casual static function with normal distribution,so the parameters of profilogramms are treated as characteristic of normal process. In this work we assessed the autocorrelation function of profilogramms, and then using the analytic form of these functions we modeled the static process.