Tri-domain prediction and optimization of nanocomposite polymers for high-performance 3D printing

) of 0.9897, RMSE of 0.0348, and MAE of 0.0219, outperforming traditional ANN, RNN, and hybrid XGBoost models by jointly predicts mechanical, thermal, and electrical properties. Results confirm the model's ability to capture physics-consistent nonlinear interactions among filler concentration, print energy density, and process temperature. This AI-driven approach provides a virtual material design framework, significantly reducing experimental trial-and-error cycles while enhancing design reliability for high-performance, multifunctional nanocomposites in aerospace, biomedical, and electronic applications.