Curved fiber paths optimization of a composite cylindrical shell via Kriging-based approach
Bibliographic record
Abstract
While conventional design and manufacturing techniques of fiber-reinforced laminates keep the fiber orientation angle constant within a layer, automated tow-placement technology allows fabricating laminates with curved fibers. This offers more flexibility to tailor the mechanical properties and improve the performance of laminated structures. Exploiting this flexibility requires an efficient method for finding optimal or near-optimal fiber configurations. In this paper, laminated cylindrical shells are studied. Curvilinear variations for the fiber orientations are adopted in the circumferential and longitudinal directions. The computational burden, typical in numerical optimization of complex structures, is reduced using a Kriging model, which substitutes for direct finite element simulation. A sequential quadratic programming algorithm is employed as local optimizer, coupled with a restart strategy to search for the global optimum in the entire design space. Some numerical cases are presented: the maximization of the fundamental frequency of the shell considering different boundary conditions and the minimization of the maximum displacement with a constraint on the buckling load.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".