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Record W1986066808 · doi:10.1115/1.4024410

Enhanced Formability in Sheet Metals Produced by Cladding a High Strain-Rate Sensitive Layer

2013· article· en· W1986066808 on OpenAlexafffund
Xiaohua Hu, Pengfei Wu, D. J. Lloyd, J.D. Embury

Bibliographic record

VenueJournal of Applied Mechanics · 2013
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsNovelis (Canada)McMaster University
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of Canada
KeywordsNeckingMaterials scienceFormabilityCladding (metalworking)Composite materialStrain rateVolume fractionSheet metalLayer (electronics)

Abstract

fetched live from OpenAlex

The necking behavior of cladding sheets with a rate-sensitive layer cladding on a rate-insensitive core material has been studied. A nonlinear long-wavelength analysis, similar to the one proposed by Hutchinson and Neale (1977, “Influence of Strain-Rate Sensitivity on Necking Under Uniaxial Tension,” Acta Metal., 25, pp. 839–846) for monolithic rate-sensitive materials, is developed to identify the onset of necking in a rate-sensitive clad sheet. This relatively simple analysis is validated by comparing its numerical results with those based on more complicated finite element analysis. It is demonstrated that for monolithic rate-sensitive materials the proposed nonlinear analysis reduces to the one developed by Hutchinson and Neale (1977). For cladding sheets, it is found that the necking strain increases monotonically by increasing the strain-rate sensitivity of the clad layer if the volume fraction of cladding is fixed. It is also revealed that, for fixed strain-rate sensitivity of the clad layer, necking localization is retarded by increasing the volume fraction of the cladding layer.

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 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)0.001
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: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.875

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.007
GPT teacher head0.191
Teacher spread0.184 · 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 teacher head, 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

Citations13
Published2013
Admission routes2
Has abstractyes

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