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Effect of Skin Depth on Hardness Profile of Gear Heated by Induction Using 2D Model

2013· article· en· W2098482469 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 · 2013
Typearticle
Languageen
FieldEngineering
TopicInduction Heating and Inverter Technology
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Rimouski
Fundersnot available
KeywordsFinite element methodElectromagnetic coilCoupling (piping)Induction heatingMaterials scienceInduction coilSkin effectPower (physics)Nonlinear systemSoftwareWork (physics)ThermalMechanical engineeringProcess (computing)MechanicsEngineeringStructural engineeringComposite materialComputer scienceElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

This work is carried principally by simulation efforts using computer-modeling software (COMSOL). The developed 2D model includes the coupling between electromagnetic and thermal fields, and takes account of the nonlinear behaviour of material properties versus temperature. Several steps were followed to reach this goal. First, a 2D finite element model of a gear was developed in order to simulate the induced currents density and temperature distributions for various frequencies and external currents applied in the coil. Second, the temperature profiles were compared using the ratio between the skin depth and the teeth height. In geometry cases, it was possible to dose the power level of the medium (MF) and high (HF) frequencies to reach a desired uniform case depth. The MF and HF powers are simulated sequentially in order to better concentrate heat in the tooth tip and root of gear. The obtained results help process developers to select the proper parameters for the induction machine in order to achieve the desired hardness profile.

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 score0.026
Threshold uncertainty score0.426

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 head0.039
GPT teacher head0.340
Teacher spread0.301 · 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