Optimizing stiffener configuration and buckling performance of composite panels under in-plane shear: A hybrid ANN-FEA approach
Bibliographic record
Abstract
Predicting buckling loads and optimizing stiffener configurations for composite stiffened panels present significant challenges due to nonlinear behavior and the computational demands of iterative simulations by traditional finite element analysis (FEA). This study addresses these challenges by integrating artificial neural networks (ANNs) with FEA to develop an efficient and accurate predictive framework. An in-plane shear load experiment was designed and conducted to validate the combined ANN-FEA model, which was further utilized to investigate buckling phenomena and provide initial predictions of critical buckling loads. The FEA results demonstrated that the stiffener configuration significantly affects load-carrying capacity, underscoring its critical role in structural performance. To reduce the computational intensity of FEA, ANN was trained on a subset of FEA-generated data, achieving high predictive accuracy for buckling loads with reduced modeling effort. The proposed hybrid approach successfully optimized stiffener parameters, offering a robust solution for improving the design and performance of composite stiffened panels under shear loading.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".