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Record W2164950758 · doi:10.1051/epjconf/20122601033

Development of a strain-gage installation method for high-speed impact of strikers on a Split Hopkinson bar apparatus

2012· article· en· W2164950758 on OpenAlexaff
M. Bolduc, Richard Arsenault

Bibliographic record

VenueEPJ Web of Conferences · 2012
Typearticle
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsSplit-Hopkinson pressure barPlasticityBar (unit)Strain gaugeAccelerationStructural engineeringMaterials scienceStress (linguistics)USableStrain rateEngineeringComposite materialComputer scienceGeologyPhysics

Abstract

fetched live from OpenAlex

When designing a split Hopkinson system, the yield strength of the selected pressure bar material determines the maximum stress attainable within deforming samples and also, the maximum striker velocity that is usable to prevent plasticity in bars. In practice, to avoid strain gages stripping, the velocity of the striker is limited by the instantaneous particles acceleration generated in the bars. As a consequence, the full potential of the Hopkinson system can not be used. Therefore, for material having very high yield strength, it is very difficult to induce the required stress wave to create plasticity using a split Hopkinson apparatus. The idea of using an improved method for installing strain gages to withstand very high accelerations is very important for high dynamic loading using the split Hopkinson bar system. This paper compares standard strain gage installation procedures with a new approach. Data obtained for a material showing no plasticity at a normal operating impact velocity range of 22 m/s while showing usable level of plasticity when using a high impact velocity of 37 m/s is discussed.

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 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 categoriesInsufficient 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 score0.086
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)0.0010.000

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 head0.059
GPT teacher head0.351
Teacher spread0.292 · 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 teacher head, not a consensus.

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

Citations1
Published2012
Admission routes1
Has abstractyes

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