An inclusive numerical framework to assess the role of feedstock features on the quality of cold spray deposits

Abstract

Cold spray technology provides unique capabilities for coating, repair, and additive manufacturing. However experimental trial and error to optimize deposit quality indexes, that rely on powder characteristics, can be costly and tedious. In this research, multiple finite element modelling approaches were compared regarding their capability and shortcomings to analyze the role of major powder features on deposit properties. Lagrangian and CEL methods were selected as the most robust approaches for analyzing the single/multi-particle impact condition, respectively. An intricate algorithm was introduced to accurately evaluate the effect of parameters including particle size, shape, oxidation extent, and impact angle on the deposition indicators such as critical velocity, particle flattening, and deposit porosity. The results exhibited a good agreement with the reported experimental data, confirming the capacity of the proposed numerical framework to tune the deposit properties as a function of a wide range of feedstock characteristics. It was concluded that for successful deposition, the effect of various powder properties and their respective effect and contribution towards plastic deformation should be taken into account. The results indicated that for a given particle velocity, smaller particle, more irregular morphology, thicker oxide layer, and smaller impact angle will result in limited particle deformation and a lower possibility of successful adhesion.

Keywords

• Supersonic deposition
• Finite element method
• Coating
• Additive manufacturing
• Powder