Reduced-order hybrid modelling for powder compaction: Predicting density and classifying diametrical hardness

Drawing on machine learning (ML) techniques and physics-based modelling, two feature-based reduced-order models are presented: one for the quantitative prediction of density and another for the classification of the diametrical hardness of pellets from a powder compaction process (pelleting). For interpretabilit y , the models use as input only the parameters from a modified Drucker-Prager Cap (DPC) model calculated from process data monitoring and the applied maximal compression force. For quantitative density prediction, 8 features linked to first-principles models of powder compaction are generated, and the final model uses only 2. A critical part of the modelling, and also one of the main contributions, is a data augmentation step for the primary data set of this study by leveraging much smaller supplementa r y data sets that have measurements not present in the primary data set.The final results imply a significant reduction in the quantity of data needed for model input and cut down the cost of data acquisition, storage, and computational time. Additionally provided is a detailed analysis of the impact and relevance of the generated features on the model performance.The density prediction model , using only 2 features ,reaches a mean absolute scaled error (MASE) of 12.9% and a mean absolute error (MAE) of 0.10 (where r2 =0.975). The scaled (diametrical) hardness classifier has an F1 score of 0.915 using 4 features.