A State-Aware Hybrid Modeling Framework for Energy Prediction in Injection Molding

Injection molding is one of the most widely used polymer manufacturing processes and is associated with substantial energy consumption, estimated at approximately 30 billion kilowatt-hours (kWh) annually in the United States. Improving the ability to predict and monitor energy usage is therefore an important step toward more sustainable manufacturing. This study investigates three predictive modeling approaches, namely physics-based (PB), data-driven (DD), and hybrid models, for estimating energy consumption in the injection molding process. The PB model captures thermodynamic and mechanical energy contributions, while the DD model learns empirical relationships from experimental data. Building on the complementary strengths of these approaches, a hybrid modeling framework is introduced that integrates PB and DD predictions via a combination of a hidden Markov model (HMM) and a probability-informed rule-based decision-making (PRBDM) mechanism. This framework is introduced as a general methodology for energy prediction in manufacturing systems with uncertainty and variability, in which predictions switch between DD and PB results according to specific rules or probabilistic latent-state inference of the process. To demonstrate the framework, experimental data from a comprehensive set of injection molding experiments involving multiple materials, processing conditions, and equipment configurations were used to train and evaluate the models. On a held-out test partition, the hybrid framework achieved a coefficient of determination (R²) of 0.6639 and a prediction accuracy of 85.51%, providing a modest improvement over the standalone DD baseline. Rather than solely maximizing predictive accuracy, the hybrid framework provides a state-aware modeling strategy for combining interpretable PB models with black-box DD predictors, which can potentially be used beyond energy prediction for quality monitoring or real-time process adjustment across manufacturing processes.