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Characterization of Hardening Behavior at Ultra-High Strain Rate, Large Strain, and High Temperature

2016· article· en· W2563138239 on OpenAlex
Ming Jun Piao, Hoon Huh, Ik Jin Lee

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 · 2016
Typearticle
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsMaterials scienceFlow stressStrain rateSofteningHardening (computing)Strain hardening exponentSplit-Hopkinson pressure barComposite materialTensile testingProjectileStrain (injury)Ultimate tensile strengthMetallurgy

Abstract

fetched live from OpenAlex

This paper is concerned with the characterization of the OFHC copper flow stress at strain rates ranging from 10 − 3 s − 1 to 10 6 s − 1 considering the large strain and high temperature effects. Several uniaxial material tests with OFHC copper are performed at a wide range of strain rates from 10 − 3 s − 1 to 10 3 s − 1 by using a INSTRON 5583, a High Speed Material Testing Machine (HSMTM), and a tension split Hopkinson pressure bar. In order to consider the thermal softening effect, tensile tests at 25°C and 200°C are performed at strain rates of 10 − 3 s − 1 ,10 1 s − 1 , and 10 2 s − 1 . A modified thermal softening model is considered for the accurate application of the thermal softening effect at high strain rates. The large strain behavior is challenged by using the swift power law model. The high strain rates behavior is fitted with the Lim–Huh model. The hardening curves are evaluated by comparing the final shape of the projectile from numerical simulation results with the Taylor impact tests.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.096
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.214
Teacher spread0.206 · 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