Résumé
In this study, we present a hybrid Physics-Assisted Machine Learning (PAML) model that integrates Deep Learning (DL) techniques with the classical Discrete Element Method (DEM) to simulate slurry drying during a lithium-ion battery electrode manufacturing process. This model predicts the microstructure evolution leading to the formation of the electrode as a time-series along the drying process. The hybrid approach consists in performing a certain amount of DEM simulation steps, nDEM, after every DL prediction, mitigating the risk of unphysical predictions, like overlapping particles. Our PAML model was rigorously tested by evaluating different functional metrics of the predicted electrodes, including density, porosity, tortuosity factor, and radial distribution function. We conducted an in-depth analysis of performance versus accuracy, particularly focusing on the impact of the nDEM hyperparameter, which represents the number of DEM steps executed between two subsequent DL predictions. Despite the model being trained on a specific formulation (96% of Active Material, AM, and 4% of Carbon Binder Domain, CBD), it demonstrated exceptional generalization capability when used to extrapolate to a different formulation (94% AM and 6% CBD). This adaptability highlights the robustness of our PAML hybrid approach. Furthermore, the integration of DL significantly reduced the computational cost versus the original DEM model simulation, decreasing the calculation time from 615 to 36 min for the whole slurry drying simulation process. Our findings underscore the potential of combining ML with traditional simulation methods to enhance efficiency and accuracy in the field of electrode manufacturing