Work

Structure–property–processing relationships for high-performance cyanate ester resin using multiscale process modeling

Year: 2026
Type: article
Abstract: High-performance cyanate ester composites are widely used in aerospace structures because of their excellent mechanical properties and thermal and dimensional stability. Multiscale approaches are required to understand the evolution of material properties and cure-induced residual stresses during composite processing. This information is necessary to optimize the composite material performance and manufacturing cycle. Predictive computational simulations are the key to providing process-dependent data over the entire processing window. In this work, an integrated multiscale process modeling framework is developed to predict the physical and mechanical evolution of a trifunctional cyanate ester composite system at the nano- and micro-scales. Experimentally-validated molecular dynamics simulations are coupled with cure kinetics to determine the evolution of mass density, post-gelation volumetric shrinkage, and mechanical properties with extent of cure, processing time, and temperature. The resulting properties are incorporated into a micromechanical modeling framework to predict the cure-dependent mechanical response of graphene nanoplatelet (GNP), graphene oxide (GO), and carbon nanotube (CNT) reinforced cyanate ester nanocomposites. The results show that the mechanical properties increase with extent of cure and decrease with increasing processing temperature. While the mechanical properties of the CNT- and GO-reinforced nanocomposites were similar, they were both slightly greater than the GNP-reinforced nanocomposite because of GNP agglomeration. The proposed workflow provides an efficient route for generating constitutive inputs required for rapid process modeling and predicting cure-induced residual stresses; and establishes a transferable framework for integrated computational materials engineering of high-performance thermosetting polymers.
Language: English
Open Access status: hybrid
OpenAlex ID: W7220458524
FWCI:
Cites: 40
Cited by:
Related to: 10