Moldflow-based simulation of flow dynamics in nano-filled polymer injection molding: a comprehensive rheological and thermal analysis

The escalating geometric complexity of injection-molded components demands high-fidelity simulation tools to predict melt flow behavior and optimize process parameters prior to tooling. This study presents a comprehensive Moldflow-based numerical investigation of melt flow dynamics in nano-filled polymer systems during injection molding of a thin-walled trapezoidal plate. Autodesk Moldflow Insight (MPI) was employed as the simulation platform, integrating experimentally calibrated Cross-WLF viscosity parameters, modified Pressure–Volume–Temperature (PVT) relationships, and filler-adjusted thermal conductivity values. Nano-filled composites containing graphene nanoplatelets, nano-silica, and MWCNTs at 1–5 wt% were investigated alongside a neat polymer baseline. Key indicators evaluated include cavity fill time, flow-front progression, pressure distribution, bulk melt temperature, shear rate profiles, clamp force evolution, solidification time, and volumetric shrinkage. Nanofiller incorporation at 3 wt% increased fill time by 13.5% (2.75 s to 3.12 s) and peak cavity pressure by 19.1% (68 MPa to 81 MPa). Enhanced thermal conductivity in nano-augmented systems accelerated wall solidification by ~ 2–3%, narrowing the processing window. Simulation predictions were validated against experimental data with a mean absolute percentage error (MAPE) of 3.4% and R² > 0.90. ANOVA confirmed significant effects of nanofiller loading (p < 0.01) and melt temperature (p < 0.05). A melt temperature increase of + 10 °C reduced injection pressure demand by 6–8% and specific energy consumption by ~ 7%. The novelty of this work lies in the systematic, multi-filler Moldflow simulation framework integrating experimentally calibrated Cross-WLF and PVT models for PP-based nano-composites, enabling the first comprehensive correlation of nanofiller-induced rheological modifications with fill dynamics, solidification, and energy consumption for thin-walled injection-molded components. The validated framework provides practical guidance for digital process optimization aligned with Industry 4.0 objectives.