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Record W2298245441 · doi:10.4236/msce.2016.43001

Study of Frequency Effects on Hardness Profile of Spline Shaft Heat-Treated by Induction

2016· article· en· W2298245441 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

VenueJournal of Materials Science and Chemical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicInduction Heating and Inverter Technology
Canadian institutionsCégep de RimouskiUniversité du Québec à Rimouski
Fundersnot available
KeywordsInduction hardeningMaterials scienceFinite element methodInduction heatingHardening (computing)Spline (mechanical)Case hardeningHardnessMechanicsMechanical engineeringMetallurgyStructural engineeringComposite materialEngineeringResidual stressElectromagnetic coil

Abstract

fetched live from OpenAlex

This paper is devoted to the study of frequency effects on hardness profile of AISI 4340 spline shaft heat-treated by induction through an extensive 3D finite element method simulation and structured experimental investigation. Based on coupled electromagnetic and thermal fields analysis, the 3D model is used to estimate the temperature distribution and the hardness profile. The proposed study examines the hardening process parameters, such as frequency, induced current density and heating time, known to have an influence on hardened surface and builds the simulation model step by step. The established model can provide not only an accurate prediction of temperature distribution and hardness profile but also a comprehensive analysis of machine parameters effects, especially the frequency. The numerical results achieved by this model are good and present a great agreement to the experimental data.

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 score0.001
Threshold uncertainty score0.252

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 head0.007
GPT teacher head0.210
Teacher spread0.203 · 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