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Record W2762964971 · doi:10.1109/iwc.2016.8068364

Non-destructive measurement of residual stresses in railway wheels by ultrasonic method

2016· article· en· W2762964971 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsIntegrity Testing Laboratory (Canada)
Fundersnot available
KeywordsResidual stressResidualUltimate tensile strengthMaterials scienceUltrasonic sensorStructural engineeringComposite materialComputer scienceEngineeringAcoustics

Abstract

fetched live from OpenAlex

While certain progress has been achieved in the development of different techniques for residual stress measurement, a considerable effort is still required to develop efficient and cost-effective methods of residual stress measurement and analysis. A new ultrasonic computerized complex UltraMARS was developed recently that can be used for non-destructive measurement of applied and residual stresses in laboratory samples and real parts and structural elements in many applications for a wide range of materials. The system was used in measurements of residual stresses in the rim of a railway wheel that was in service without intensive drag braking. The measurements showed that depth of compressive hoop stresses is 26mm from the rolling surface with a maximum value of -60 MPa. Similar measurements of residual stresses in the rim of railway wheel with intensive drag braking showed that the hoop residual stresses in rolling surface layers are tensile.

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.

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 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 score0.115
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.016
GPT teacher head0.255
Teacher spread0.239 · 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

Quick stats

Citations3
Published2016
Admission routes1
Has abstractyes

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