An Analytical Technique for Optimization of Mechanical Performance of Foam Core Sandwich Structures
Bibliographic record
Abstract
Sandwich structures are widely used, especially in areas where the performance of conventional materials is simply not adequate. Sandwich components achieve the same structural performance as conventional materials with weight savings of up to 75 %. They are basically made from two thin skins (faces) and a lightweight thicker core. Their structural, physical, and mechanical characteristics can be tailored based on service requirements by selection of different materials and manufacturing processes. In this study, the geometry and property of each separate component is utilized to the structural advantage of the whole assembly. Although Lagrangian method has been widely applied in other engineering disciplines, it has received less attention for optimization of sandwich components. The Lagrangian method is therefore introduced and expanded to find solutions for multipurpose design problems. This new optimization approach will enable us to find analytical solutions for complicated design problems which were conventionally solved by utilizing graphical methods. This paper aims to present a generic optimization method which can be used in the variety of applications in this field.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".