Microstructure‐Resolved Modeling and Prediction of Thermal Conductivity in PA6 /Ag Nanocomposites via SEM ‐Based RVE Reconstruction and Finite Element Analysis

ABSTRACT Efficient thermal management is increasingly critical for polymer‐based components used in compact electronic and semiconductor devices. Here, we develop an integrated experimental‐computational framework to quantify and predict heat‐transfer behavior in polyamide‐6/silver (PA6/Ag) nanocomposites. Silver nanoparticles were incorporated at 5, 10, and 15 wt%, increasing the thermal conductivity of PA6 from 0.23 W m −1 K −1 to 0.29, 0.39, and 0.56 W m −1 K −1 , respectively. A deterministic image‐processing workflow was used to extract particle‐size distributions and dispersion characteristics from SEM micrographs, which were directly employed to reconstruct representative volume elements (RVEs) with realistic nanoparticle morphologies. Steady‐state finite element simulations performed on these image‐derived RVEs captured the evolution of heat‐transfer pathways with increasing filler content from isolated particle conduction at low loadings to the formation of quasi‐percolated, multiscale conductive networks at 15 wt%. The simulations showed strong agreement with experimental data, whereas classical effective‐medium models significantly underestimated the composite conductivity. The results reveal that thermal enhancement in PA6/Ag nanocomposites is governed not only by filler fraction but also by microstructural evolution, including agglomeration scale and interparticle connectivity. This microstructure‐informed framework provides a predictive route for designing thermally efficient polymeric materials for electronic housings, thermal interface layers, and advanced heat‐dissipation components.