Micro-Geometric Modeling of Textile Preforms with Vacuum Bag Compression: An Application of Multi-chain Digital Element Technique
Bibliographic record
Abstract
[Abstract] Increasing demands on composite technology necessitate structural materials possessing both great ductility and extreme strength. To fully understand the mechanical behavior of 3-D textile composites, it is essential to perform analyses such as prediction of effective material properties and characterization of damage initiation and growth based on highly accurate fabric geometry. Most textile composites used in the aircraft industry are made by laminating multiple fabric layers, which are nested randomly, flattened, border stitched, compacted by rigid molding or flexible vacuum bags. The effects of the forming process on the yarn geometry are investigated. In this paper, we present a novel numerical approach to predict fabric geometry under vacuum bag compression. Textile fabric layers were modeled as multiple digital chains and nested randomly. The vacuum bag was represented as a fishing-net-like structure. The contact elements between the vacuum bag and fabrics were established. The vacuum pressure was applied to each knot on the net structure evenly. The results show that the textile fabrics are indeed deformed dramatically during the vacuum bagging compression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".