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Record W3046331642 · doi:10.1520/jte20190924

Analytical and Experimental Investigation of Elastic–Plastic Strain Distributions at 2-D Notches

2020· article· en· W3046331642 on OpenAlexaff
Dale L. Ball, Marcias Martinez, Alessandro Baldassarre, D. M. Dubowski, Scott Carlson

Bibliographic record

VenueJournal of Testing and Evaluation · 2020
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsMaterials scienceStrain (injury)Structural engineeringPlasticityLevy–Mises equationsComposite materialForensic engineeringEngineeringFracture mechanicsStress intensity factor

Abstract

fetched live from OpenAlex

Abstract Both elastic and elastic–plastic stress–strain response were studied for a series of part-circular and V-shaped notches in flat specimens. Both the peak strain at the notch root and the strain distribution along the notch bisector were studied. These quantities were determined analytically using detailed finite element analysis (FEA), and it was shown that significant differences can arise between detailed FEA results and two of the commonly used approximations for the peak notch strain response: Neuber’s rule and Glinka’s equivalent strain energy density theorem. These differences can become significant for notches with low acuity. The strain response for these configurations was also studied experimentally using two measurement techniques: optical fibers and surface differential displacement mapping. Agreement was shown between computed response strains and measured strains for some of the eight notch configurations and two aerospace alloys studied, while for others, experimental difficulties prevented agreement. These difficulties are described in detail.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.078
GPT teacher head0.285
Teacher spread0.207 · 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
Published2020
Admission routes1
Has abstractyes

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Same venueJournal of Testing and EvaluationSame topicFatigue and fracture mechanicsFrench-language works237,207