Modelling Tubular Braided Composites Using Geometrical, Micro-Computed Tomography and Finite Element Analysis Methods
Bibliographic record
Abstract
This thesis investigates various modelling approaches for two-dimensional Tubular Braided Composite (TBC) structures. Because of TBCs' flexible nature, modelling them properly is challenging and usually involves some assumptions. However, having an accurate modelling procedure would help researchers and industries obtain more accurate simulation results before manufacturing or using TBCs. In this research, the gap in the modelling of TBCs will be addressed. Some equations are available in the literature to generate geometrical models of TBCs. However, the intricate nature of the equations involved makes creating accurate geometrical models challenging. This research introduces a user-friendly and open-source software, TBC-Gen, which streamlines the modelling process, eliminating the need for extensive knowledge of the underlying equations. Moreover, some modifications have been applied to the equations to enhance the precision of TBC simulations. Comparative analyses are conducted between TBC-Gen outputs, results from other software, and physical TBC samples to investigate the accuracy of the TBC-Gen results. Micro-computed tomography (µCT) proves to be a precise method for scanning TBCs. This study employs µCT to scan various TBCs with different patterns, dimensions, and materials. Different image processing techniques have been developed to extract essential parameters (minor and major yarn and mandrel diameter, center point, orientation, and cross-sectional area of yarns) from the scanned models. Also, an innovative segmentation and splitting algorithm is implemented for overlapping yarns. Subsequently, two yarns from the scanned TBCs are extracted, and their paths are plotted. A simulated yarn path is fitted to the extracted path, and a new parameter is introduced to the geometrical model to enhance the fitness of the yarn paths. The error between the fitted geometrical model and the segmented yarn path is less than 1%. The TBC-Gen program is then utilized to design different geometrical models, and Finite Element Simulations (FEM) are developed to analyze their behaviour under tensile tests. Periodic Boundary Conditions (PBC) are applied to optimize computational efficiency. The impact of braid angle on TBC displacement during tensile testing is investigated by simulating seven models with similar geometries but varying braid angles (30°-70°). Additionally, three TBCs with similar geometries but different patterns are simulated, and their displacements are reported. The result shows that the displacement of simulated TBCs increases as the braid angle increases. Also, the Hercules pattern shows the most displacement and the Diamond pattern shows the least displacement under a similar tensile test. The FEM results are further validated by simulating the setup of an experimental test and comparing the outcomes against both experimental and Classical Laminate Plate Theory (CLPT) results. The FEM results are closer than the CLPT results to the experimental results. This comprehensive research not only advances TBC modelling methodologies but also validates their accuracy through a combination of advanced imaging techniques, innovative algorithms, and rigorous simulations, contributing valuable insights to the field of composite materials. The TBC-Gen program developed in this study can help other researchers and industries generate geometrical models without a deep understanding of the details of the equations. It can also be used for educational and visualization purposes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".