Two-Stage Machine Learning for Thermo-Mechanical Properties Optimization of Carbon-Filled Polyimide Composites.

The rational design of high-performance carbon-filled polyimide (CF/PI) composites is challenged by complex interactions among processing parameters, composition, and interfacial effects. Here, we address thermo-mechanical co-optimization in CF/PI composites through two-stage processing and formulation design. An experimental dataset was employed to construct processing and composition datasets. A CatBoost model was developed to relate hot-pressing parameters to the tensile strength (TS) of pure PI, enabling inverse optimization of processing conditions, with quantitative experimental validation of the predicted processing windows (RMSE = 3.89 MPa). Within the optimized processing window, an XGBoost-based composition-property model was further established to perform high-throughput screening and multi-objective optimization of TS and thermal conductivity (TC). To quantitatively account for interfacial effects, the mass ratio of carbon-filled to sizing agent (CF/SA) was introduced as a composition-derived feature. Model interpretation reveals that CF and graphene positively contribute to TC but negatively affect TS. Notably, formulations with CF/SA > 2 tend to achieve a more favorable TS-TC balance. Experimental validation of model-selected composite formulations confirmed the predicted trends for both TS and TC. These findings suggest that interfacial regulation is a key lever for balancing mechanical strength and thermal transport in CF/PI composites.