Maximization of Fundamental Frequency of Composite Stiffened Hypar Shell with Cutout by Taguchi Method
Bibliographic record
Abstract
Composite shells find extensive application in modern civil, aerospace, and marine structures. In order to avoid resonance, such load-carrying shells need to be optimized from a frequency perspective. Composite shell structures often include cutouts for different functional requirements. Obtaining the best combination of design variables like degree of orthotropy, ply orientation, shallowness of the shell, and eccentricity of cutout of laminated shells leads to a problem of combinatorial optimization. This article attempts a numerical study of the free vibration response of composite stiffened hypar shells with cutout using finite element procedure and optimization of different parametric combinations based on the Taguchi approach. Numerical investigations are carried out following the L27 Taguchi design with four design factors, viz., fiber orientation, width/thickness factor of shell, degree of orthotropy, and position of the cutout for different edge constraints. For different shell boundaries considered here, the width/thickness factor emerges as the most influencing factor followed by a degree of orthotropy. The optimum parametric combination for the maximum fundamental frequency of cutout borne stiffened hypar shell is obtained from the analysis.
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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 | 0.001 |
| 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 | 0.000 | 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".