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General thermomechanical model of FSW based on a characteristic temperature for deformation and heat transfer

2023· article· en· W4377019002 on OpenAlexaff
Sofia Salazar, Diego Rojas, Patricio F. Méndez, Karem Tello

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHeat transferFriction stir weldingMaterials scienceMechanicsShear (geology)WeldingWork (physics)ThermodynamicsComposite materialPhysics

Abstract

fetched live from OpenAlex

Abstract This paper presents a coupled model of heat transfer and plastic deformation in friction stir welding (FSW), accounting for the temperature profile in the substrate near the pin. This approach is analogous to the boundary layer analysis in fluid mechanics and is based on the methodology of scaling and calibration based on published data. A model focusing on common conditions in FSW, such as relatively slow translation and high rotation velocities, a thin shear layer and the influence of the shoulder on the maximum temperature was reformulated. This paper extends previous work by considering the heat flow into the pin and an improved criterion for determining the temperature at the edge of the shear layer. The results are a set of updated closed-form expressions for the maximum temperature, the thickness of the shear layer, the shear stress around the pin, torque and thermal effect of the shoulder, applicable to all metals. The predictions from this model are verified against a comprehensive database of published experiments. Applications of this model also include the accelerated determination of procedure variables and the generalization of maps of process limits.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score0.511

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 head0.016
GPT teacher head0.221
Teacher spread0.205 · 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 designBench or experimental
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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