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Record W2295372726 · doi:10.4271/2005-01-0081

Numerical and Experimental Investigation of Strain-Path Effects on Localized Deformation in Steel Alloys

2005· article· en· W2295372726 on OpenAlexaff
Kaan Inal, K.W. Neale, Cory J. Hamelin, A.K. Pilkey, B.J. Diak

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2005
Typearticle
Languageen
FieldMaterials Science
TopicMicrostructure and mechanical properties
Canadian institutionsQueen's UniversityUniversité de Sherbrooke
Fundersnot available
KeywordsMaterials scienceTexture (cosmology)Deformation (meteorology)Finite element methodPath (computing)Strain (injury)Composite materialMetallurgyStructural engineeringComputer scienceEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">A numerical approach to simulate texture evolution in BCC sheet materials subjected to complex forming strain paths is demonstrated. Drawing-quality (DQ) steel sheet is deformed experimentally through the application of a two-stage, orthogonal strain path. Bulk texture measurements before and after the test capture the crystallographic orientations. Both the undeformed bulk texture and the strain path are used as input for a finite element (FE)/grain model, which defines a unit cell that characterizes micro-structural material behavior. The simulated texture evolution after deformation is then compared with experimental data, and found to be in good agreement.</div>

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.010
GPT teacher head0.235
Teacher spread0.225 · 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.

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

Citations3
Published2005
Admission routes1
Has abstractyes

Explore more

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