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Record W2766624510 · doi:10.1063/1.5007979

Variation of strain rate sensitivity index of a superplastic aluminum alloy in different testing methods

2017· article· en· W2766624510 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

VenueAIP conference proceedings · 2017
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsSuperplasticityStrain rateMaterials scienceSensitivity (control systems)Strain (injury)ViscoplasticityComposite materialAlloyStress relaxationMetallurgyStructural engineeringConstitutive equationCreepFinite element methodElectronic engineeringEngineering

Abstract

fetched live from OpenAlex

The strain rate sensitivity index, m-value, is being applied as a common tool to evaluate the impact of the strain rate on the viscoplastic behaviour of materials. The m-value, as a constant number, has been frequently taken into consideration for modeling material behaviour in the numerical simulation of superplastic forming processes. However, the impact of the testing variables on the measured m-values has not been investigated comprehensively. In this study, the m-value for a superplastic grade of an aluminum alloy (i.e., AA5083) has been investigated. The conditions and the parameters that influence the strain rate sensitivity for the material are compared with three different testing methods, i.e., monotonic uniaxial tension test, strain rate jump test and stress relaxation test. All tests were conducted at elevated temperature (470°C) and at strain rates up to 0.1 s−1. The results show that the m-value is not constant and is highly dependent on the applied strain rate, strain level and testing method.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.833
Threshold uncertainty score0.660

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0000.000

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 head0.045
GPT teacher head0.309
Teacher spread0.264 · 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