Evaluating Three DOE Methodologies: Optimization of a Composite Laminate under Fabrication Error
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT This article aims at systematically comparing three different designs of experiments (DOE) methodologies used in the optimization of engineering structures. The selected methods are a response surface methodology, an adaptive one-factor-at-a-time search methodology, and a Bayesian DOE-based methodology. Six evaluative criteria are defined for the comparison. To perform the study, a simulation-based layout optimization of a four-layer composite laminate is used under a fiber misalignment fabrication error. It is found that each solution method satisfies a particular aspect of the six evaluative criteria and, thus, an application of a multiple criteria decision aid model is suggested to assist the analyst.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it