MétaCan
Menu
Back to cohort
Record W2770707585 · doi:10.1002/adem.201700834

Corrugation Reinforced Composites: A Method for Filling Holes in Material‐Property Space

2017· article· en· W2770707585 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAdvanced Engineering Materials · 2017
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNeckingMaterials scienceComposite materialFinite element methodReinforcementStrain hardening exponentWork hardeningHardening (computing)Structural engineeringMicrostructure

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.244
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.276
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it