MétaCan
Menu
Back to cohort
Record W2086477554 · doi:10.1115/1.4000926

Enhanced Ductility in Sheet Metals Produced by Cladding a Ductile Layer

2010· article· en· W2086477554 on OpenAlexafffund
X. X. Chen, P.D. Wu, D. J. Lloyd, J.D. Embury, Yonggang Huang

Bibliographic record

VenueJournal of Applied Mechanics · 2010
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsNovelis (Canada)McMaster University
FundersNatural Sciences and Engineering Research Council of CanadaOntario Centres of Excellence
KeywordsNeckingMaterials scienceCladding (metalworking)Composite materialVoid (composites)Strain hardening exponentSheet metalPlane stressDuctility (Earth science)Work hardeningFinite element methodUltimate tensile strengthStructural engineeringMicrostructureCreep

Abstract

fetched live from OpenAlex

The effect of cladding a ductile layer on necking and fracture in sheet metals under plane strain tension is studied numerically using the finite element method based on the Gurson damage model. It is demonstrated that the cladding increases both the necking and fracture strains. The increase in necking strain is due to the fact that cladding a ductile layer enhances the overall work hardening for the layered metal sheets according to the rule of mixtures. Furthermore, the increase in necking strain slows down the development of the triaxial tensile stress inside the neck, which delays the void nucleation and growth, and which, in turn, contributes to enhancement in ductility.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.241
Teacher spread0.231 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Applied MechanicsSame topicMetal Forming Simulation TechniquesFrench-language works237,207