Micromechanical Behavior of Fiber-Reinforced Composites using Finite Element Simulation and Deep Learning

dc.contributor.authorSepasdar, Rezaen
dc.contributor.committeechairShakiba, Maryamen
dc.contributor.committeememberHebdon, Matthew Hardyen
dc.contributor.committeememberCase, Scott W.en
dc.contributor.committeememberSeidel, Gary D.en
dc.contributor.departmentCivil and Environmental Engineeringen
dc.description.abstractThis dissertation studies the micromechanical behavior of high-performance carbon fiber-reinforced polymer (CFRP) composites through high-fidelity numerical simulations. We investigated multiple transverse cracking of cross-ply CFRP laminates on the microstructure level through simulating large numerical models. Such an investigation demands an efficient numerical framework along with significant computational power. Hence, an efficient numerical framework was developed for simulating 2-D representations of CFRP composites' microstructure. The framework utilizes a nonlinear interface-enriched generalized finite element method (IGFEM) scheme which significantly decreases the computational cost. The framework was also designed to be fast and memory-efficient to enable simulating large numerical models. By utilizing the developed framework, the impacts of a few parameters on the evolution of transverse crack density in cross-ply CFRP laminates were studied. The considered parameters were characteristics of fiber/matrix cohesive interfaces, matrix stiffness, $0^{circ}$~plies longitudinal stiffness. We also developed a micromechanical framework for efficient and accurate simulation of damage propagation and failure in aligned discontinuous carbon fiber-reinforced composites under loading along the fibers' direction. The framework was validated based on the experimental results of a recently developed 3-D printed aligned discontinuous carbon fiber-reinforced composite as the composite of interest. The framework was then utilized to investigate the impacts of a few parameters of the constitutive equations on the strength and failure pattern of the composites of interest. This dissertation also contributes towards improving the computational efficiency of CFRP composites' simulations. We exhaustively investigated the cause of a convergence difficulty in finite element analyses caused by cohesive zone models (CZMs) which are commonly used to simulate fiber/matrix interfaces in CFRP composites. The CZMs' convergence difficulty significantly increases the computational burden. For the first time, we explained the root of the convergence difficulty and proposed a simple technique to overcome the convergence issue. The proposed technique outperformed the existing methods in terms of accuracy and computational cost. We also proposed a deep learning framework for predicting full-field distributions of mechanical responses in 2-D representations of CFRP composites based on the geometry of the microstructures. The deep learning framework can be used as a surrogate to the expensive and time-consuming finite element simulations. The proposed framework was able to accurately predict the stress distribution at an early stage of damage initiation and the failure pattern in representations of CFRP composites microstructure under transverse tension.en
dc.description.abstractgeneralCarbon fiber-reinforced polymers (CFRPs) are materials that are lightweight with excellent mechanical performance. Hence, these materials have a wide range of applications in various industries such as aerospace, automotive, and civil engineering. The extensive use of CFRPs has made them an active area of research and there have been great efforts to better understand and improve the mechanical properties of these materials over the past few decades. Therefore, CFRP materials and their manufacturing process are constantly changing and new types of CFRPs are kept being developed. As a result, the mechanical behavior of CFRPs needs to be exhaustively investigated to provide guidelines for their optimal engineering design and indicate the future direction of manufacturing improvements. This dissertation studied the mechanical behavior of CFRPs through high-fidelity simulations. Two types of CFRP were investigated: laminates and 3-D printed CFRPs. Laminates are the most popular type of CFRPs which are commonly used to construct the body of aircraft. 3-D printed CFRPs are new types of material that are gaining traction due to their ability to construct structures with complex geometries at high speed and without direct human supervision. The numerical simulations of CFRPs under mechanical loading are time-consuming and require significant computational power even when run on a supercomputer. Hence, this dissertation also contributes to improving the computational efficiency of numerical simulations. To decrease the computational cost, we proposed a technique that can significantly speed up the numerical simulations of CFRPs. Moreover, we utilized artificial intelligence to develop a new framework that can be substituted for the expensive and time-consuming conventional numerical simulations to quickly predict specific mechanical responses of CFRPs.en
dc.description.degreeDoctor of Philosophyen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.subjectCFRP Compositesen
dc.subjectFE Simulationsen
dc.subjectDeep Learningen
dc.titleMicromechanical Behavior of Fiber-Reinforced Composites using Finite Element Simulation and Deep Learningen
thesis.degree.disciplineCivil Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.nameDoctor of Philosophyen
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