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Record W2117300279 · doi:10.1051/epjconf/20122601020

Characterization of impact behaviour of armour plate materials

2012· article· en· W2117300279 on OpenAlexaff
M.N. Bassim, M. Bolduc, G. Nazimuddin, Jeffrey Delorme, Ioannis Polyzois

Bibliographic record

VenueEPJ Web of Conferences · 2012
Typearticle
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsDefence Research and Development CanadaUniversity of Manitoba
Fundersnot available
KeywordsAdiabatic shear bandMaterials scienceSplit-Hopkinson pressure barAlloyComposite materialMetallurgyStrain rateShearing (physics)Torsion (gastropod)Deformation (meteorology)AluminiumPlasticityDeformation mechanismMicrostructure

Abstract

fetched live from OpenAlex

Three armour plate materials, including two steels, namely HHA and Mars 300, and an aluminium alloy 5083, were studied under impact loading to determine their behaviour and the mechanisms of deformation that lead to failure. The experimental testing was carried out using either a direct impact compression Split Hopkinson Bar or a torsion Hopkinson Bar. The impact properties and stress-strain cures were obtained as a function of the impact momentum in compression and the angle of twist in torsion. It was found that at the high strain rates developed in the specimen during the tests, the deformation occurs by the formation of adiabatic shear bands (ASBs) which may lead to the formation of cracks within the bands and the ultimate failure of the specimens. It was also found that below a certain impact momentum, the deformation is more uniform and no ASBs are formed. Also, ASBs are more likely to form in the BCC metals such as the two steels while diffuse ASBs associated with plastic flow are exhibited in the 5083 aluminum alloy. Microstructural techniques ranging from optical microscopy to atomic force microscopy (AFM) were used to study the topography of the ASBs. Also, modelling of the formation was performed. The results provide a comprehensive understanding of the role of ASBs in the failure of these 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.

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 score0.253
Threshold uncertainty score0.994

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)0.0070.000

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 head0.034
GPT teacher head0.302
Teacher spread0.268 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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