Data-Driven Optimization of Industrial Impact Polypropylene Characterization: Machine Learning Insights

The experimental determination of impact polypropylene (ICP) physical properties, such as tensile modulus, flexural modulus, and impact strength, is a time-sensitive process that can delay real-time decision making during industrial production. This study explores the use of machine learning (ML) models that facilitate real-time determination of these key parameters. An industrially relevant data set containing ICP structural properties, including melt flow rate (MFR), ethylene content (C2), ethylene content in the rubber component (RCC2), and the amorphous phase indicator (R21), was leveraged to train and evaluate three ML models; linear regression, Random Forest, and a neural network. Random Forest emerged as the best-performing model, achieving <i>R</i>² values of 0.78 (tensile modulus), 0.75 (flexural modulus), and 0.88 (impact strength). Feature importance analysis via Random Forest and SHapley Additive exPlanations (SHAP) revealed that MFR and R21 captured the most critical structural variation across all physical properties and were sufficient for accurate model prediction. Retraining the model with only these two features significantly reduced model complexity and experimental overhead. These models offer a generalizable, scalable, and interpretable solution for real-world deployment across different ICP production sites, utilizing only two input parameters determined via ISO-certified methods. This ML-based approach significantly enhances process efficiency, reduces reliance on multiple characterization experiments, and supports digital product development in industrial ICP manufacturing.