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Record W4392024426 · doi:10.24018/ejeng.2024.9.1.3116

Experimental Investigation of Void Coalescence in XTral-728 Plate Containing Three-Void Cluster

2024· article· en· W4392024426 on OpenAlexfundno aff
Mohammad Atiqur Rahman, Md Mesbah Uddin, Latiful Kabir

Bibliographic record

VenueEuropean Journal of Engineering and Technology Research · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVoid (composites)The VoidCoalescence (physics)Cluster (spacecraft)Materials scienceMechanicsComposite materialPhysicsComputer scienceAstronomyOperating system

Abstract

fetched live from OpenAlex

Void clustering effect is investigated by performing uniaxial tensile test on commercial 95% aluminum alloy XTral 728 by strategically placing an additional hole. Incorporating additional void induces strong stress and strain localization and reduces incipient coalescence strain approximately 17%. Also, two different cluster orientations with respect to applied loading and hole spacing ratios have been considered. The experimental revealed that material ductility is significantly decreased with increasing hole spacing ratios and for certain cluster orientation. To evaluate existing void coalescence models, numerical simulations are also performed and found that existing models overestimate the incipient coalescence strains for considered three-void cluster.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.271
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2024
Admission routes1
Has abstractyes

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