Cold Spraying of Mixed Metal Powders: A Modelling Framework for Predicting Deposition Efficiency and Coating Composition

Abstract

Cold spraying (CS) of composite coatings produced from mixed metal powders can exhibit enhanced functional properties over coatings made from pure metals. However, controlling the deposition efficiency and the resulting microstructure during CS is challenging due to interactions between different materials. In this study, we developed a modelling framework to predict the deposition efficiency (DE) of mixed metal powders and the resultant coating composition. This is achieved by predicting the critical and impact velocities as a function of particle size, which allows determination of the DEs of both matched (A/A or B/B) and mismatched (A/B or B/A) particle/substrate combinations. These DEs are then used to determine the overall DE of the composite coating and its composition by using a layer-wise deposition model. The modelling framework is validated by performing several CS experiments using Cu and Al particles together with SEM image analyses of the coating microstructures. We find that in-flight interaction of particles of different masses has a significant effect on the impact velocity and hence DE of composite coatings. By effectively predicting DE and coating composition, the proposed model serves as a valuable tool for optimizing cold spray parameters, reducing trial-and-error costs and time, and accelerating the development of novel composite coatings with enhanced properties.