Abstract
A stochastic method is introduced to characterize the dual-scale geometry of textile reinforcements in composites. The fiber tows are identified automatically from X-ray microtomographic scans with a machine learning algorithm, quantifying the error of the procedure. The tow geometry is then used to construct a stochastic model as a Gaussian Random Process which permits quantification of the uncertainty in the measurements of microscale fiber volume fraction. The hyperparameters of the model are calibrated with a custom-built multi-objective evolutionary algorithm. The approach is illustrated by the analysis of a vinyl-ester composite reinforced with a glass fiber non-crimp fabric.
Original language | English (US) |
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Article number | 111031 |
Journal | Composite Structures |
Volume | 224 |
DOIs | |
State | Published - Sep 15 2019 |
All Science Journal Classification (ASJC) codes
- Ceramics and Composites
- Civil and Structural Engineering