In this work, a multiscale modeling approach is applied for effective nano-coating development in the automotive industry. By this approach, a set of multiscale models are developed to establish comprehensive correlations among material properties (at nano-to-macroscale), application conditions (usually at macroscale), and coating performances (at meso-to-macroscale). The macroscopic oven curing model set includes an air flow turbulence model, a radiation heating model and a panel heating model using CFD. This model can generate location specific coating temperature information. A lattice Monte Carlo with bond fluctuation model is utilized to construct the coating nano-to-mesoscale structure, i.e., the connectivity in the polymer network, spatial distribution of nanoparticles, and polymer chain conformation. A coating quality model correlates coating structure with the final coating property (e.g., stiffness). The information passed from the macroscopic curing condition model to the Monte Carlo code is generated through a macro-micro integration method.
The effects of material parameters and curing conditions on the final coating stiffness are thoroughly investigated. Simulation has revealed that when the nanoparticle and polymer have attractive interactions, the coating stiffness is steadily increased as increasing nanoparticle concentration. It is also suggested by simulation that the coating quality uniformity throughout the vehicle body can be improved through changing curing conditions (i.e., the direction of the air sprayed from the nozzles). These insights are especially valuable for identifying improved material formulation and application conditions, so that experiment based nano-coating development can be greatly assisted and product development cycle time can be significantly reduced.