## 1 Introduction
To evaluate the effect of gas inlet conditions, simulations were performed under two nozzle inlet settings: 2 MPa and 400 \({}^{\circ}\)C, and 4 MPa and 800 \({}^{\circ}\)C. Particle behavior was effectively modeled using a two-way coupled Eulerian-Lagrangian approach. The findings revealed that nozzle inlet conditions had the greatest impact on particle velocity, temperature, and powder deposition. Under high-pressure conditions (4 MPa), DE exceeded 99.9% across all the test cases, ensuring nearly complete particle adherence to the substrate. In contrast, at medium pressure (2 MPa), DE increased linearly with mask SOD, attributed to the rise in particle velocity upon impact. While CFD and modern experimental equipment effectively capture particle in-flight behavior, predicting particle velocity and temperature upon impact under various conditions requires numerous simulations and experiments due to the many parameters involved in the process. A comprehensive exploration of these parameters, both individually and collectively, is challenging owing to the high computational cost, long processing time of simulations, and the expense and extended duration of experimental measurements. To address these limitations, machine learning (ML) provides a promising alternative by leveraging data-driven approaches to identify relationships between parameters like inlet pressure, substrate SOD, mask SOD, mask properties, and deposition characteristics. ML has been utilized to predict particle velocity in plasma and cold spray processes. [PERSON] et al. [14] employed Residual Neural Network (ResNN) and Support Vector Regression (SVR) approaches, and used CFD simulations of the plasma spray process to generate training data. Despite the stochastic nature of the process, both models achieved high accuracy in predicting average particle velocity, with R\({}^{2}\) values of 97% and 99%, though their Mean Absolute Percentage Error (MAPE) remained around 20% on unseen data. For cold spray, [PERSON] et al. [15] applied ML classification algorithms to distinguish between deposited and not-deposited particles based on kinetic and thermal energies obtained from CFD simulations, achieving classification accuracies of 98-99% for aluminum and copper particles. Moreover, [PERSON] et al. [16] applied SVR and neural network (NN) to predict particle velocity distribution and deposition efficiency in the cold spraying of titanium using experimental data. While the models accurately predicted the average particle velocity, they struggled with single-particle velocity due to stochastic effects. This study aims to develop a stacked ML model for the rapid and accurate prediction of particle spatial, velocity, and temperature distributions upon impact on a substrate in masked cold spray additive manufacturing. This ML framework comprises two primary models. The first model predicts the spatial distribution of particles on the substrate. The output of this model then serves as the basis for the second model, which forecasts the particle velocity and temperature distributions upon impact. Unlike classical cold spray deposition on a flat substrate, masked cold spray introduces additional complexity, requiring advanced modeling to capture the intricate interactions between particles, masks, and the substrate. This ML framework is trained on a dataset of 48 CFD simulations from our previous study [13], with multiple strategies employed to enhance its predictive capabilities. This approach significantly reduces computational costs and processing time while offering strong adaptability to dynamic boundary conditions, making it a scalable and efficient solution for optimizing the cold spray process [17] and enhancing the electrocatalytic performance of nickel-based electrodes. A detailed discussion of the methodology and training of the ML model is provided in the following section, while the results of the chosen ML models are presented in the results section.