Automated draping analysis of 3D printed flexible isogrid structures for textile applications
Bibliographic record
Abstract
Fused filament fabrication (FFF) 3D printing can be used for manufacturing flexible isogrid structures. This work presents a novel draping analysis of flexible 3D printed isogrids from thermoplastic polyurethane (TPU) using image processing. A small-scale multi-camera automated draping apparatus (ADA) is designed and used to characterize draping behavior of 3D printed isogrid structures based on draping coefficient (DC) and mode. Circular specimens are designed and 3D printed that accommodate up to eight additional weights on their perimeters to enhance draping. Five infill patterns, three infill percentages, and three loading cases are explored to evaluate their impact on specimens’ draping coefficient and mode, resulting in 45 tests. The range of DCs in this study is 21.9% to 91.5%, and a large range of draping modes is observed. For the lowest infill percentage, specimen mass is not the sole contributor to the DC values and the infill pattern has a significant impact for the three loading cases. Considering draping modes, the maximum number of convex and concave nodes observed for 25% infill specimens with added weights is three. The draping behavior characterization developed in this study can be followed to design and 3D print new flexible isogrids with textile applications.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".