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Record W2423064448 · doi:10.1016/j.procir.2016.02.074

Development of a Procedure to Characterize Residual Stresses Induced by Drilling

2016· article· en· W2423064448 on OpenAlexaff
Mathieu Girinon, Frédéric Valiorgue, J. Rech, Éric Feulvarch

Bibliographic record

VenueProcedia CIRP · 2016
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsSafran Electronics (Canada)
FundersCentre Technique des Industries Mécaniques
KeywordsResidual stressHole drilling methodDeep hole drillingResidualDrillingMaterials scienceDiffractometerElectropolishingStrain gaugeMicrostructureCharacterization (materials science)Sample (material)Structural materialStructural engineeringComposite materialComputer scienceMetallurgyEngineeringAlgorithmPhysicsScanning electron microscopeNanotechnology

Abstract

fetched live from OpenAlex

Modeling residual stresses induced by drilling remains an issue. However, even if some models are under development, their validation is limited by the absence of a universal characterization method of residual stresses inside the hole. This paper aims at presenting a procedure to characterize the residual stress profile induced by drilling. The principle is based on the preparation of a reference sample that has been pre-heat treated in order to remove bulk residual stresses without modifying its microstructure. Then the sample is instrumented with strain gauges before being cut into two parts. This enables on the one hand to estimate the elastic recovery after cutting and on the other hand to provide an easy access to the surface investigated. Finally a standard X-Ray diffractometer and an electropolishing technique are combined to estimate residual stress profiles in two directions (tangential and ortho-radial directions).

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)0.002
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: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.020
GPT teacher head0.230
Teacher spread0.210 · 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
GenreMethods

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

Citations15
Published2016
Admission routes1
Has abstractyes

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Same venueProcedia CIRPSame topicWelding Techniques and Residual StressesFrench-language works237,207