A hybrid mesh-based framework for accurate and convenient macro-shape prediction of cold spray deposits

Accurate prediction of deposition geometry is essential for achieving dimensional control in cold spray additive manufacturing, repair and welding. Various approaches have been lately implemented to address this gap. However, there is still considerable space for improvement as the existing analytical and numerical models either lack generalizability or incur high computational costs, while most machine learning methods commonly require large experimental datasets and often disregard underlying physics. This study presents an efficient computational framework to overcome these limitations. First, a numerical approach is developed to conveniently extract the empirical parameters of the analytical shape-estimating model using only a single experimental deposition profile. This approach enables the derivation of the deposit shape parameters and the relative deposition efficiency as a function of impact angle. In the second phase, a mesh-based simulator is introduced, incorporating these parameters along with an enhanced shadowing algorithm tailored for thick and complex depositions. The robustness of the parameter extraction method is validated through a parametric study, and the predictive capability of the model is verified against experimental data for pure copper and titanium alloy Ti6Al4V under various spraying conditions. Moreover, various demonstrations highlight the utility of the predictive model for potential toolpath planning to control the deposition shape. The proposed framework enables accurate simulation of the deposition profiles with minimum experimental input, offering an efficient and physically consistent tool for cold spray deposit shape prediction and control. • A two-phase modeling framework is proposed for cold spray deposit shape prediction. • Shape parameters are derived from only one single experimental deposition profile. • A mesh-based simulator predicts complex deposits with high fidelity. • The model predictions show strong agreement with copper and Ti6Al4V experiments. • The approach reduces experimental demand while improving prediction accuracy.