Curvilinear Variable Stiffness 3D Printing For Improved Mechanical Performance
Bibliographic record
Abstract
Continuous Curvilinear Variable Stiffness (CCVS) is proposed as a novel design technique to generate Variable Stiffness design for improving the performance of composite panels featuring open-hole cut-outs. Compared to existing VS design techniques, CCVS steers the fibers around the cut-out without breaking at the holes using only a single design variable the geometry. The technique utilises a numerical method known as Source Panel method, which is typically utilised in the fluid dynamics world. Utilising this technique, the performance of an open hole ASTM D5766 coupon manufactured using Fused Filament Fabrication (FFF) was improved 16-38% depending on the ratio of the hole to the width of the specimen. The technique was further improved on to allow for arbitrary geometries such as fuselage cut-outs. A fuselage cut-out case was examined, and it was shown that a CCVS design can improve the performance over a QuasiIsotropic design by 57%. To validate CCVS, it is necessary to first manufacture and validate the part. This was done by developing a robotic 3D printing work-cell capable of 5 axis of material deposition of both thermoplastic and pre-impregnated carbon fiber. Finally, an in-process inspection technique was developed using a laser line scanner in the work-cell for the purposes of quality control.
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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".