Physics-Informed Machine Learning for Property Prediction and Process Optimization in Additively Manufactured Filled Polymer Composites: A Bayesian Optimization Approach

The development of filled photopolymer composites for Digital Light Processing (DLP) additive manufacturing requires optimizing processing parameters to achieve the desired mechanical properties. Traditional experimental approaches are time-intensive, while physics-based models often struggle to capture the complex interactions among parameters. This study presents a physics-informed machine learning framework that combines Random Forest with Bayesian optimization (RF-BO) to predict the ultimate tensile strength in recycled thermoset resin composites manufactured via DLP. A validation dataset of 19 systematically varied formulations (each with n = 5 measurement replicates for reliability) was generated and augmented with 1500 physics-informed synthetic samples to enable robust model training. The limited experimental dataset, while insufficient for traditional statistical inference, provided critical validation of physical trends, including non-monotonic particle-size effects and optimal processing windows. Six machine learning algorithms were evaluated, with RF-BO achieving superior performance (R2 = 0.9125, MSE = 1.07 MPa). The framework identified optimal processing conditions of 59–64 μm particle size, 5.0 ± 0.5 wt.% concentration, and 60 min cure time, predicting a maximum UTS of 43.84 MPa with a prediction error of less than 1.0 MPa. Feature importance analysis revealed that cure time was the dominant parameter (40%), followed by particle size (30%), validating the physical interpretability. This approach demonstrates significant potential for accelerating materials design in composite additive manufacturing while maintaining physically meaningful predictions.