A hybrid computational approach for modeling cold spray deposition

Cold spray (CS) is a cold-state technology that uses high-velocity supersonic gas flow to propel and deposit powder particles onto a substrate surface. The design and optimization of the cold spray deposition process were recently achieved via the experimental trial-and-error approach, which is laborious and costly. The present study presents a computationally efficient hybrid method for simulating cold spray coating deposition, applied explicitly to Ni coating used for wear applications. The model effectively synergizes meshless and meshed computational schemes in predicting the thermo-mechanical deformation of the impacting Ni particles and SS304 steel substrate. During the simulations, the point cloud (PC) of deformed particle domains is converted into a high-quality finite element (FE) mesh with novel PC processing algorithms. The simulations are carried out for various particle characteristics and spraying conditions. The numerical predictions are validated by comparing them with other numerical schemes and previous experimental studies. The main results indicate that the kinetic energy and morphology of the impacting Ni particles strongly influence plastic deformation and temperature rise in the substrate and predeposited coating particles. Plastic deformation is more prominent at the particle edges and mating material interfaces. At the same time, the temperature rise does not reach the melting point but can allow for recrystallization near highly localized regions of the coating microstructure, even at lower deposition rates. Compressive residual stresses are also observed across the coating and substrate layers, with a non-uniform and nonlinear residual stress field due to complex interactions among neighboring particles. The study aims to provide a robust numerical framework for designing and optimizing cold spray deposition parameters for industrial coatings, bridging the knowledge gap and enabling efficient and cost-effective simulations for process optimization.