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On the Measurement of Residual Stress in Induction Hardened Parts

2011· article· en· W2072674161 on OpenAlexafffund
Vincent Savaria, Majid Hoseini, Florent Bridier, Philippe Bocher, Patrick Arkinson

Bibliographic record

VenueMaterials science forum · 2011
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of CanadaPratt and Whitney Canada
KeywordsResidual stressMaterials scienceInduction hardeningDiffractionComposite materialStress (linguistics)NucleationQuenching (fluorescence)BendingOptics

Abstract

fetched live from OpenAlex

It is well known that induction surface heating followed by rapid quenching generally increases the fatigue life of steel components subjected to bending loads by significantly postponing the micro-crack nucleation and propagation processes. The phase transformation volume change combined with severe thermal gradients leave a hard surface layer under relatively high and deep compressive residual stresses. In this paper, residual stress measurements are done on induction hardened AMS6414 martensitic steel (aerospace grade of AISI4340) cylinders using two techniques: the so-called contour method and X-ray diffraction. For both methods, induction hardened parts raise many challenges. The contour method hardly describes high stress gradients near the surface while the diffraction technique accuracy appears limited considering the strong microstructural variation and the high depth of the stresses to be measured. For the contour method, a CMM and an optical pen using the confocal chromatic imaging principle were used to measure the surface after precision WEDM cutting. The effect of data filtering and smoothing on the calculated stresses are discussed. For X-ray analysis, the effect of stress relaxation during layer removal and analysis technique is explained. The difference between the residual stress measurements done with the two techniques is discussed with emphasis on both the surface and the in-depth measurements.

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 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 score0.004
Threshold uncertainty score0.179

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 head0.047
GPT teacher head0.230
Teacher spread0.184 · 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 teacher head, 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

Citations10
Published2011
Admission routes2
Has abstractyes

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