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Due to the anisotropic and heterogeneous nature of fiber-reinforced polymer composites, failure prediction of this class of materials is extremely challenging under general loading conditions. In addition, composite strength/failure data is notorious for showing wide variability, one major reason for which is the morphological variability existing in its microstructures. In this dissertation, failure prediction of composites was undertaken for static and fatigue loading situations. For the case of static loading, a computational framework was further developed to link the randomness observed in the composite microstructure morphology and the resultant variability in its mechanical properties, specifically transverse elastic moduli and failure strengths. For the case of fatigue loading, a kinetic theory of fracture based fatigue material model was developed and applied to the problem of fatigue damage prediction in open hole tension coupons comprised of multidirectional laminates undergoing tension-tension loading. Based off lamina-level material characterization data, cumulative damage analysis of matrix and fiber constituents under cyclic loading conditions was implemented with the objectives to: (i) detect the onset and the progression of subcritical matrix failures in the form of matrix cracks and delamination, (ii) predict the ultimate failure due to fracture of the reinforcing fibers, and (iii) observe the effects of the interactions among damage modes on the overall failure progression process. In addition to the above physics-based approaches, machine learning based models were explored as potential alternatives to analytical failure theories to learn from the experimental data and predict biaxial failure envelopes of unidirectional composite laminas under quasi-static loading.
Product Details :
Genre | : Carbon fiber-reinforced plastics |
Author | : Faisal H. Bhuiyan |
Publisher | : |
Release | : 2021 |
File | : 236 Pages |
ISBN-13 | : 9798516068355 |