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Record W2942705086 · doi:10.3390/met9050488

A Comparison between Anodizing and EBSD Techniques for Primary Particle Size Measurement

2019· article· en· W2942705086 on OpenAlexaff
Shahrooz Nafisi, Anthony Roccisano, Reza Ghomashchi, George Vander Voort

Bibliographic record

VenueMetals · 2019
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversity of Alberta
FundersFlinders UniversityAustralian National Fabrication Facility
KeywordsMetallographyElectron backscatter diffractionMaterials scienceGrain sizeOptical microscopeAlloyMetallurgyEtching (microfabrication)MicroscopyDiffractionParticle sizeOpticsScanning electron microscopeComposite materialMicrostructureGeology

Abstract

fetched live from OpenAlex

Proper understanding and knowledge of primary particle or grain size is of paramount importance in manufacturing processes as it directly affects various properties including mechanical behavior. Application of optical microscopy coupled with etching techniques has been used conventionally and in conjunction with color metallography (polarized microscopy) has been the preferred method for grain size measurement. An advanced technique as an alternative to light microscopy is using electron backscatter diffraction (EBSD). A comparison is made between these two techniques using Al-7Si alloy produced with various casting techniques to highlight the cost and time of the sample preparation and analysis for both techniques. Results showed that color metallography is certainly a faster technique with great accuracy and a much cheaper alternative in comparison with EBSD.

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)0.002
metaresearch head score (Gemma)0.003
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.036
GPT teacher head0.253
Teacher spread0.217 · 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

Citations12
Published2019
Admission routes1
Has abstractyes

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