Prediction of powder flow of pharmaceutical materials from physical particle properties using machine learning

Understanding powder flow and how it affects pharmaceutical manufacturing process performance remains a significant challenge for industry. This work aims to improve decision making for manufacturing route selection, achieving the key goal of digital design within Industry 4.0 of being able to better predict properties whilst minimizing the amount of material required and time to inform process selection during early-stage development. A Machine Learning model approach is proposed to predict the flow properties of new materials from their physical properties. The model’s implementation will enhance manufacturing quality by taking advantage of the data generated throughout the manufacturing process.