Open Collections

Olfatbakhsh, Tina

University of British Columbia

Multiscale properties of fabric-reinforced composites are commonly modeled via numerical and experimental methods, which are often highly time-consuming and complex. In this thesis, a materials informatics-based approach has been developed, and applied through two case studies, to link the micro/meso-level features of woven fabric composites to their effective Young’s moduli. In the first (experimental) case study, the microstructures of a set of glass fiber-reinforced polymer samples were imaged using micro-computed tomography, with the property of interest being the elastic modulus obtained from tensile tests. The data-driven framework included establishing a reduced-order quantification of microstructure using two-point spatial correlations and three different principal component analysis techniques. Next, various regression models were implemented and compared to predict the material microstructure-property (modulus) relationship via the captured images. Partial Least Squares was overall the best performing model, which contained components that are well correlated with woven fabrics' geometrical attributes while providing an agreeable prediction of Young's modulus at different fiber orientations and different fiber volume fractions. In the second (numerical) case study, the selected best model from the first study was applied to a simulation-based dataset. Namely, the 3D geometry of three-layer composite laminates was modeled using TexGen software. The tensile property of the laminate, on the other hand, was simulated using Abaqus finite element analysis suite. Two-point spatial correlations in 3D and Partial Least Squares were applied to link the meso-level geometry of the structure to the mechanical property. Similar to the first study, despite the limited number of samples, the data-driven model led to predictions with highly interpretable components and excellent accuracy for different ply orientations, regardless of apparent uncertainties such as waviness, yarn cross-section and width variations. The findings appear to be a promising step forward for the potential use of materials informatics for smart design and optimization of woven fabric composites in prominent industries, including aerospace and transportation.

Text

2022-10-04

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/82812

Mechanical Engineering

University of British Columbia

UBCO

http://creativecommons.org/licenses/by-nc-nd/4.0/