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Sensitivity Study of Temperature Profile of 4340 Spur Gear Heated by Induction Process Using 3D Model

2012· article· en· W2069098669 on OpenAlexaff
Noureddine Barka, Ahmed Chebak, Abderrazak El Ouafi, Philippe Bocher, Jean Brousseau

Bibliographic record

VenueApplied Mechanics and Materials · 2012
Typearticle
Languageen
FieldEngineering
TopicInduction Heating and Inverter Technology
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Rimouski
Fundersnot available
KeywordsSensitivity (control systems)Electromagnetic coilInduction heatingSpur gearMaterials scienceThermalProcess (computing)Induction coilMechanical engineeringMechanicsEngineeringComputer scienceThermodynamicsElectronic engineeringPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

This paper presentsa sensitivity study using a Comsol3D model simulation for spur gear heated by induction process. Based on an adequate formulation and taking into account the material properties, a multi-physics 3D model is built to calculate the final temperature distribution determinate according the machine parameters and some geometrical factors (coil width and gap between coil and gear). Since the hardness profile is affected by thermal historic during heating, the surface temperatures are deeply analyzed versus the initial current density and the heating time using medium (MF) and high frequencies (HF). Finally, the sensitivity of hardness profile with the machine parameters variation isinvestigated using various statistical tools applied to the obtained results. The obtained results exhibits the main machine parameters and theirs effects on the 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.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.228
Teacher spread0.212 · 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 designSimulation or modeling
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

Citations6
Published2012
Admission routes1
Has abstractyes

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