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Microscopic Strain and Crystal Rotation Measurement within Metallurgical Grains

2013· article· en· W2035951717 on OpenAlexaff
Florent Bridier, Jean‐Charles Stinville, Nicolas Vanderesse, Patrick Villechaise, Philippe Bocher

Bibliographic record

VenueKey engineering materials · 2013
Typearticle
Languageen
FieldComputer Science
TopicOptical measurement and interference techniques
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsDigital image correlationMicroscale chemistryMaterials scienceScanning electron microscopeAustenitic stainless steelElectron backscatter diffractionRotation (mathematics)Ultimate tensile strengthComposite materialSlip (aerodynamics)MetallurgyCrystallographyMicrostructureGeometryCorrosionPhysicsChemistry

Abstract

fetched live from OpenAlex

This work describes an experimental procedure to measure the progressive strain localization and crystal lattice rotation within metallurgical grains. A digital image correlation software was implemented and associated with mechanical tests carried out inside a scanning electron microscope on specimens exhibiting nanometric grainy patterns. Cross-correlation analyzes between electron backscattering diffraction maps were also developed to quantify the corresponding local crystal rotation relative to the original structure. The microscale strain and rotation fields on the surface of a tensile-loaded specimen made of austenitic stainless steel 316L are presented as an illustration. Their direct spatial correlation between strain heterogeneities and the progressive activation of slip systems is put into evidence and discussed.

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 score0.002
Threshold uncertainty score0.006

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)0.0020.000

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 head0.023
GPT teacher head0.213
Teacher spread0.190 · 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

Citations16
Published2013
Admission routes1
Has abstractyes

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