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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. 

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. 

The transverse tensile strength of composites is susceptible to size effects. Therefore, it is paramount to develop length-scale specific physical test procedures to validate computational models that estimate the transverse composite response using micromechanics. To this end, a computational process modeling and virtual mechanical testing framework are presented in this study to predict the transverse response of composite microstructures subjected to processing conditions. Informed by a comprehensive material dataset, the numerical model is shown to reliably predict the process-induced residual stress generation in composite microstructures and accurately evaluate its influence on their transverse strength prediction. A novel procedure to fabricate thin composite laminates from a single ply of carbon fibers and characterize their transverse tensile response is presented to validate the numerical model. The results show excellent agreement with the virtual test predictions. This study highlights the importance of length-scale specific testing to minimize the influence of size effect on the transverse composite strength. 

The effect of residual stress build-up on the transverse properties of thermoset composites is studied through direct and inverse process modeling approaches. Progressive damage analysis is implemented to characterize composite stiffness and strength of cured composites microstructures. A size effect study is proposed to define the appropriate dimensions of Representative Volume Elements (RVEs). A comparison between periodic (PBCs) and flat (FBCs) boundary conditions during curing is performed on converged RVEs to establish computationally efficient methodologies. Transverse properties are analyzed as a function of the fiber packing through the nearest fiber distance statistical descriptor. A reasonable mechanical equivalence is achieved for RVEs consisting of 40 fibers. It has been found that process-induced residual stresses and fiber packing significantly contribute to the scatter in composites transverse strength. Variation of ±5% in average strength and 18% in standard deviation are observed with respect to ideally cured RVEs that neglect residual stresses. It is established that process modeling is needed to optimize the residual stress state and improve composite performance. 

Molecular dynamics simulation of a thermoset network and the glass transition by heating and cooling.