Layer Separation for Optimization of Composite Laminates
Bibliographic record
Abstract
The excellent mechanical properties of laminated composites cannot be exploited without a careful design of stacking sequence of the layers. An important variable in the search of the optimum stacking sequence is the number of layers. The larger is this number, the harder as well as longer is the search for an optimal solution. To tackle efficiently such a variable-dimensional problem, we introduce here a multi-level optimization technique. The proposed method, called Layer Separation (LS), increases or decreases the number of layers by gradually separating a layer into two, or by merging two layers into one. LS uses different levels of laminate representation ranging from a coarse level parameterization, which corresponds to a small number of thick layers, to a fine level parameterization, which corresponds to a large number of thin layers. A benefit of such differentiation is an increase of the probability of finding the global optimum. In this paper, LS is applied to the design of composite laminates under single and multiple loadings. The results show that LS convergence rate is not inferior to that of other optimization techniques available in the literature. It is faster than an evolutionary algorithm, more efficient than a layerwise method, simple to perform, and it has the advantage of possibility of termination at any point during the optimization process.
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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) | 0.001 | 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".