Corrugation Reinforced Composites: A Method for Filling Holes in Material‐Property Space
Bibliographic record
Abstract
Material‐property space is filled with holes representing desirable combinations of properties, such as high strength and high necking strain. One way to fill those holes is to use architectured materials. In this work, Finite Element Modeling (FEM) simulations are performed to evaluate composites with a corrugated reinforcement architecture across a range of volume fractions and corrugation heights for a model copper‐steel system. The corrugated reinforcement geometry shows large improvements in necking strain, which increases with corrugation height, without sacrificing strength, and fills a desirable region in material‐property space. Additionally, it is found that the necking strain of a matrix material can be increased by adding a less ductile reinforcing material provided it has a highly corrugated geometry. The improvement in necking strain seen in these composites is attributed to a boost in work hardening that results from an evolving reinforcement alignment as the corrugation unbends.
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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 | 0.000 |
| 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".