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The Effects of Specimen Geometry on the Accuracy of Tensile Testing of Metallic Superplastic Materials

2010· article· en· W2024878952 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueKey engineering materials · 2010
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsSuperplasticityMaterials scienceTensile testingDeformation (meteorology)Ultimate tensile strengthGauge (firearms)Composite materialMetallurgyStrain gaugeAlloy

Abstract

fetched live from OpenAlex

This work investigates the sensitivity of a superplastic material’s tensile test to the major geometrical parameters of the selected test specimen. This required generating a large number of specimens by systematically varying the gauge length, gauge width, grip length and width of a standard geometry. The specimens were prepared from a moderately superplastic AZ31B-H24 magnesium alloy sheet and were then stretched at a selected rate and temperature. Deformation in each specimen was tracked via an electrochemically-etched fine grid which was particularly used to quantify the amount of material flow from the grip into the gauge region. The consequences of the latter on the accuracy of measured stresses and strains were correlated back to the corresponding geometrical parameters. Ultimately, the results were utilized to set the guidelines for selecting the optimum parameters in a “proper” specimen, for testing the unique class of superplastic materials.

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.

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)0.003
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 score0.003
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.178
Teacher spread0.171 · 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