Micro Mechanical Model of Filament Wound Composite Pipe With Damage Analysis
Bibliographic record
Abstract
Filament winding (FW) is one of the most common techniques for manufacturing composite pipes. The material properties and failure mechanism of composite pipes depend largely on winding pattern. In this study a micro mechanical approach for filament wound composites (FWCs) is pursued. A diamond-shaped repeated unit cell (RUC) is first constructed which characterizes the micro architecture of FWC pipe, such as winding angel, shift between successive circuits and the area of local undulation region. The micro mechanical model is embedded into commercial FEM code of ABAQUS as user-defined subroutine thus the link between the analyses in macro engineering structural scale and in micro material structural scale is established. By averaging micro stiffness constants over the cell macro ones needed for engineering structural analysis can be obtained. On the other hand, the macro structural analysis provides average stresses/strains of the cell locating at any concerned region of the macro structure for local stress and damage analysis. Effects of tow undulation caused by tow crossover on micro stresses are taken into accounted. The model is applied to glass/epoxy wound pipes with various winding angles and winding shifts. Mechanical properties are predicted and damage evolutions are simulated. The effects of delamination damage, usually introduced by lateral low velocity impact, on stiffness and ultimate strength of FWC pipe are also investigated.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".