Exploring the filler morphology and temperature‐dependent compressive response of glass‐filled epoxy composites: Insights from experiments and viscoplastic simulations

Abstract

AbstractThis study aims to improve the compressive strength of epoxy at operating temperatures below the glass transition temperature by introducing micron‐sized spherical and milled glass fillers at varying volume fractions. Test specimens were prepared by mixing spherical and milled fillers up to 20% and 15% volume fractions. The room temperature (27°C) results showed that 15% and 10% volume fractions of spherical and milled filler‐reinforced epoxy composites, respectively, exhibited the highest stress‐bearing ability among their respective filler volume fractions and hence were further tested at 45°C and 60°C. 15% spherical and 10% milled fillers enhanced epoxy's yield strength from 103 MPa to 111 MPa and 123 MPa at 27°C and from 67 MPa to 73 MPa and 80 MPa at 60°C. Scanning electron microscopic imaging revealed matrix cracking and filler breakage as the primary failure mechanisms at ambient temperature, while matrix softening at 60°C caused filler‐matrix debonding to dominate. The Three‐Network viscoplastic model was used as matrix property in ABAQUS to simulate the representative volume elements reinforced with milled and spherical fillers at mentioned temperatures. The modulus, yield, and strain softening stresses for both filler‐reinforced epoxy composites are well predicted by the simulations. Simulations revealed that milled fibers endure higher compressive stress than spherical particles at the given temperatures, although matrix stress remains almost the same.Highlights Milled fiber epoxy composites exhibit higher yield strength than spherical ones. 10% milled fiber endures the highest strength among all the fillers used. Matrix and filler breakages are witnessed at ambient temperature. Filler‐matrix debonding dominates at elevated temperature. Three‐Network model captures well the milled and spherical epoxy composite's experimental data.