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Record W4386850744 · doi:10.3390/engproc2023043026

Modelling Induction Heating of Aluminium Sheets for Hot Stamping

2023· article· en· W4386850744 on OpenAlexaff
A. Gariépy, Guillaume D’Amours

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsInduction heatingBlankAluminiumFormabilityMaterials scienceInduction coilElectromagnetic coilHeating elementInduction hardeningStampingHot stampingThermalComposite materialMetallurgyMechanical engineeringThermodynamicsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Hot forming of aluminium sheets enables the forming of complex shapes with high-strength alloys due to increased formability at elevated temperatures. A fast, uniform and accurate heating method is required to meet the narrow solution heat treatment window from 460 to 490 °C for AA7xxx alloys before forming. Heated plates and convection furnaces are commonly used in hot forming, but require large equipment and can have medium to long cycle times. Induction heating, which uses an alternating current in a coil surrounding the workpiece, could provide shorter heating times. However, induction heating requires a part-specific coil and can exhibit significant temperature gradients over the blank. Multi-physics electromagnetic and thermal simulation can be used as a design tool to help achieve the target temperature uniformity. In this study, induction heating of an irregular aluminium blank was modelled and validated with experimental data. Methods to improve the temperature uniformity were also tested numerically.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.249

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 head0.054
GPT teacher head0.251
Teacher spread0.197 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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