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Sample preparation and image acquisition using optical‐reflective microscopy in the measurement of fiber orientation in thermoplastic composites

2012· article· en· W2119615292 on OpenAlexfundno aff
Gregorio M. Vélez-García, Peter Wapperom, Vlastimil Kunc, Donald G. Baird, Audrey Zink-Sharp

Bibliographic record

VenueJournal of Microscopy · 2012
Typearticle
Languageen
FieldComputer Science
TopicOptical measurement and interference techniques
Canadian institutionsnot available
FundersSaudi Basic Industries CorporationDivision of Civil, Mechanical and Manufacturing InnovationNational Science FoundationBayer CanadaU.S. Department of EnergyOffice of Energy Efficiency and Renewable Energy
KeywordsMaterials scienceComposite materialThermoplasticOrientation (vector space)FiberEllipseOptical microscopeGlass fiberEtching (microfabrication)Matrix (chemical analysis)OpticsScanning electron microscopeGeometryMathematics

Abstract

fetched live from OpenAlex

A complete sample preparation procedure used to determine three-dimensional fiber orientation from optical micrographs of glass fiber-reinforced thermoplastic composites is presented. Considerations for elimination of irregularities in the elliptical footprints, contrast enhancement between fibers and surrounding polymer matrix, controlled-etching that allows the identification of small shadows where fiber recedes into the matrix, and topographical reconstruction of the elliptical footprint are described in the procedure. This procedure has produced high-quality optical micrographs employed to obtain accurate fiber orientation data for thermoplastic composites using the method of ellipses. The optimal definition of the nonelliptical footprints' borders allows an accurate measurement of orientation in small sampling areas.

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)0.001
metaresearch head score (Gemma)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.039
GPT teacher head0.343
Teacher spread0.305 · 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
GenreMethods

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

Citations33
Published2012
Admission routes1
Has abstractyes

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