Quantitative characterization of surface morphological evolution is critical for coating quality control. A hybrid MC-CFD (i.e.

Monte Carlo coupled with computation fluid dynamics) method was developed to capture the complex coating formation process

which involves wet film formation through deposition of huge amount of droplets and the succeeding levelling process. The effects of the mean diameter

viscosity

density and surface tension of paint droplets on coating roughness evolution have been systematically and quantitatively explored. It was found that increasing mean drop diameter led to the increase of the initial surface roughness

hence increased levelling time; the increase of paint viscosity led to the increased initial and final surface roughness and hence increased levelling time; paint density had little influence on levelling time and final surface roughness; increasing surface tension of paint however led to the increase of levelling velocity and the decrease of levelling time

but had little effect on final coating roughness.