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Record W2077556803 · doi:10.1115/imece2014-37678

Prediction of Hardness Profile of 4340 Steel Plate Heat Treated by Laser Using 3D Model and Experimental Validation

2014· article· en· W2077556803 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

VenueVolume 2B: Advanced Manufacturing · 2014
Typearticle
Languageen
FieldEngineering
TopicLaser and Thermal Forming Techniques
Canadian institutionsUniversité du Québec à Rimouski
Fundersnot available
KeywordsMaterials scienceDiagramScanning electron microscopeNonlinear systemPower (physics)Continuous cooling transformationMetallurgyMechanicsComposite materialMicrostructureComputer scienceThermodynamicsMartensite

Abstract

fetched live from OpenAlex

The paper presents a study of hardness profile of 4340 steel plate heat treated by scanning laser technique using 3D model. The proposed approach is carried out in three distinguished steps. First, a commercial software 3D model was developed using an adequate formulation and taking into account the nonlinear behaviour of the material. Second, the hardness curve is approximated from the temperature distribution using metallurgical assumptions related to the kinetic transformation and the temperature-time transformation diagram. Then, the case depth is analyzed quantitatively versus the beam power density and scanning speed. Finally, the developed approach is validated using experimental tests. The gap between simulation and experience results is determined. The obtained results allow predicting of the hardness profile with a fairly good precision.

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 score0.155
Threshold uncertainty score0.665

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 head0.012
GPT teacher head0.218
Teacher spread0.206 · 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