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Modeling and Sensitivity Study of the Induction Hardening Process

2006· article· en· W2013476420 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

VenueAdvanced materials research · 2006
Typearticle
Languageen
FieldEngineering
TopicInduction Heating and Inverter Technology
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Rimouski
Fundersnot available
KeywordsFinite element methodInduction hardeningHardening (computing)Materials scienceResidual stressSensitivity (control systems)Induction heatingNonlinear systemMicrostructureMartensiteMechanical engineeringStructural engineeringResidualMaterial propertiesMetallurgyComposite materialEngineeringComputer scienceLayer (electronics)AlgorithmElectronic engineering

Abstract

fetched live from OpenAlex

Induction heating is a case hardening process used to improve performance of machine components by producing a hard martensitic microstructure and high compressive residual stresses at the surface layer. A reliable numerical model able to predict the hardness profile would shorten process development. However, the accuracy and the efficiency of the model are restricted by the coupling complexity between the electromagnetic and thermal fields, and the nonlinear behaviour of the material properties. The paper analyzes the sensitivity of the material properties values and of the finite element meshing onto the predictive modeling of the case hardening profiles. The material used is SAE-4340 low-alloy steel. The simulations are done using a computer-modeling software (Comsol) and the sensitivity analysis is conducted by using an experimental design method.

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)0.000
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 score0.077
Threshold uncertainty score0.190

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.039
GPT teacher head0.322
Teacher spread0.283 · 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