Numerical Investigation of Air-Assisted Atomization Spraying Deposition of Nano-Al2O3 Composite Coatings: Effects of Process Parameters on Coating Thickness Uniformity

Abstract

Uniform deposition of nanoparticle suspensions over large-area substrates remains challenging because coating formation is strongly influenced by suspension properties and spraying conditions. Therefore, this study employed computational fluid dynamics (CFD) to simulate the air-assisted spray deposition of nano-Al2O3 suspensions, with method validation. The effects of nanoparticle volume fraction, feed rate, and air pressure on deposition morphology and coating thickness distribution were investigated. Increasing the nanoparticle volume fraction from 1% to 4% improved thickness uniformity, reducing the normalized root-mean-square error and coefficient of variation (CV) to 5.52% and 38.90%, respectively, while a further increase to 5% provided no additional improvement. Increasing the feed rate from 1 g/s to 5 g/s increased the mean coating thickness from 3.09 μm to 10.97 μm and the effective coverage area from 2.5 × 104 mm2 to 4 × 104 mm2, but intensified local thickness accumulation. Increasing the air pressure from 0.1 MPa to 0.3 MPa enhanced droplet dispersion, increasing the effective coverage area from 2.45 × 104 mm2 to 3.57 × 104 mm2 while decreasing the mean coating thickness from 10.25 μm to 6.99 μm. These findings provide a reference for process selection in large-area nanoparticle suspension deposition.