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Record W2766977593 · doi:10.1115/pvp2017-65177

Effects of Notch Acuity on Fracture Toughness Measurements

2017· article· en· W2766977593 on OpenAlexaff
Dong-Yeob Park, Jean‐Philippe Gravel, Da-Ming Duan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsTransCanada (Canada)Natural Resources Canada
Fundersnot available
KeywordsMaterials scienceFracture toughnessComposite materialToughnessCompact tension specimenElectrical discharge machiningCrack growth resistance curveFracture mechanicsCrack closureMachiningMetallurgy

Abstract

fetched live from OpenAlex

Effects of initial crack-tip acuity on toughness measurements has been evaluated through extensive small-scale toughness testing — single-edge notched tension and bend (SE(T) and SE(B)) — at room temperature using two X70 pipeline steels and girth welds. In order to investigate effects of different crack tip radii on toughness, some specimens were notched to the target initial crack size using electrical discharge machining (EDM), and no further fatigue precracking was made. On the other hand, other specimens were EDM-notched and then fatigue-precracked to the target crack size according to the current practice of ASTM E1820. The results show that effects of crack tip acuity on toughness measurement are significant especially at the crack-tip blunting stage. The EDM precrack increased toughness measurements by a factor of up to 1.6 and 2 for SE(B) and SE(T), respectively, at 0.2 mm of crack extension, compared to the fatigue precrack.

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)0.006
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.246
Teacher spread0.224 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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