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Abstract High-performance epoxy systems are extensively used in structural polymer‒matrix composites for aerospace vehicles. The evolution of the thermomechanical properties of these epoxies significantly impacts the evolution of process-induced residual stresses. The corresponding process parameters need to be optimized via multiscale process modeling to minimize the residual stresses and maximize the composite strength and durability. In this study, the thermomechanical properties of a multicomponent epoxy system are predicted via molecular dynamics (MD) simulation as a function of the degree of cure to provide critical property evolution data for process modeling. In addition, the experimentally validated results of this study provide critical insight into MD modeling protocols. Among these insights, harmonic- and Morse-bond-based force fields predict similar mechanical properties. However, simulations with the Morse-bond potential fail at intermediate strain values because of cross-term energy dominance. Additionally, crosslinking simulations should be conducted at the corresponding processing temperature, because the simulation temperature impacts shrinkage evolution significantly. Multiple analysis methods are utilized to process MD heating/cooling data for glass transition temperature prediction, and the results indicate that neither method has a significant advantage. These results are important for effective and comprehensive process modeling within the ICME (Integrated Computational Materials Engineering) and Materials Genome Initiative frameworks. 

Chemical shrinkage in thermosetting polymers drives residual stress development and induces residual deformation in composite materials. Accurate characterization of chemical shrinkage during curing is therefore vital to minimize residual stresses through process modeling and optimize composite performance. This work introduces a novel methodology to measure the pre- and post-gelation chemical shrinkage of an epoxy resin using three-dimensional digital image correlation (3D-DIC). Differential scanning calorimetry (DSC) is employed to calculate reaction kinetics and correlate chemical shrinkage with the degree of cure. Rheology experiments are conducted to quantify gelation and validate post-gelation. 3D-DIC post-gelation results show excellent agreement with rheology. Pre-gelation results show the effect of the in-situ curing in the proximity of constraints on the global strain behavior. This work introduced an innovative approach to characterize the chemical shrinkage of thermosets during curing, which will enable accurate residual stress prediction for enhancing thermoset composite performance and provide insight into the in-situ polymer behavior during processing. 

Uncontrolled curing-induced residual stress and strain are significant limitations to the efficient design of thermoset composites that compromise their structural durability and geometrical tolerance. Experimentally validated process modeling for the evaluation of processing parameter contributions to the residual stress build-up is crucial to identify residual stress mitigation strategies and enhance structural performance. This work presents an experimentally validated novel numerical approach based on higher-order finite elements for the process modeling of fiber-reinforced thermoset polymers across two composite characteristic length scales, the micro and macro-scale levels. The cure kinetics is described using an auto-catalytic phenomenological model. An instantaneous linear-elastic constitutive law, informed by time-dependent material characterization, is used to evaluate the stress state evolution as a function of the degree of cure and time. Micromechanical modeling is based on Representative Volume Elements (RVEs) that account for random fiber distribution verified against traditional 3D FE analysis. 0/90 laminate testing at the macroscale validates the proposed approach with an accuracy of 9%. 

The transverse strength of fiber-reinforced composites is a matrix-dominated property whose accurate prediction iscrucial to designing and optimizing efficient, lightweight structures. State-of-the-art analytical models for compositestrength predictions do not account for fiber distribution, orientation, and curing-induced residual stress that greatlyinfluence damage initiation and failure propagation at the microscale. This work presents a novel methodology to develop an analytical solution for transverse composite strength based on computational micromechanics that enables the modeling of stress concentration due to representative volume elements (RVE) morphology and residual stress. Finiteelement simulations are used to model statistical samples of composite microstructures, generate stress-strain curves,and correlate statistical descriptors of the microscale to stress concentration factors to predict transverse strength as a function of fiber volume fraction. Tensile tests of thin plies validated this approach for carbon- and glass-reinforced composites showing promise to obtain a generalized analytical model for transverse composite strength prediction.