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Record W3042379864 · doi:10.1115/hvis2019-051

Shear band characteristics in high strain rate naval applications

2019· article· en· W3042379864 on OpenAlexaff
Michael J. P. Conway, James D. Hogan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsShear bandShear (geology)Materials scienceStrain hardening exponentYield (engineering)Strain rateShear stressComposite material

Abstract

fetched live from OpenAlex

Abstract This paper explores the dynamic behavior of HSLA 65 naval steels, specifically focusing on the initiation and growth of shear bands in quasi-static and dynamic compression experiments and how these bands affect stress-strain responses. The results indicate that the yield strength for this HSLA 65 increases from 541 ± 8 MPa for quasi-static (10-3 s-1) to 1081 ± 48 MPa for dynamic rates 1853 ± 31 s-1, and the hardening exponent increases from 0.376 ± 0.028 for quasi-static to 0.396 ± 0.006 for dynamic rates. Yield behavior was found to be associated with the onset of shear banding for both strain-rates, confirmed through visualization of the specimen surface using high-speed and ultra-high-speed cameras. For the quasi-static case, shear banding and yielding was observed to occur at 2.5% strain, and were observed to grow at speeds of upwards of 38 mm/s. For the dynamic experiments, the shear banding begins at approximately 1.18 ± 0.06% strain and these can grow upwards of 2122 ± 213 m/s during post-yield softening. Altogether, these measurements are some of the first of their kind in the open literature, and provide guidance on the critical time and length scales in shear banding. This information can be used in the future to design more failure-resistant steels, which has broader applications in construction, defense, and natural resource industries.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.425
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0070.005

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 head0.013
GPT teacher head0.261
Teacher spread0.249 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2019
Admission routes1
Has abstractyes

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