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Record W2017750341 · doi:10.1142/s1758825110000767

STRESS–STRAIN EVOLUTION IN CAST IN-738 SUPERALLOY SINGLE FUSION WELDS

2010· article· en· W2017750341 on OpenAlexafffund
E. A. Bonifaz, N.L. Richards

Bibliographic record

VenueInternational Journal of Applied Mechanics · 2010
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceResidual stressWeldingPlasticitySuperalloyvon Mises yield criterionFinite element methodFusionComposite materialResidualStress (linguistics)MechanicsStructural engineeringMicrostructurePhysicsMathematics

Abstract

fetched live from OpenAlex

A three-dimensional finite element elastic–plastic model was developed to study localized plastic strains and residual stresses that exist in a body that has previously been subjected to nonuniform temperature changes. The mechanical model was used to compute the evolution of plastic strains and residual stresses of welded material. The thermal gradient histories calculated during the GTA welding of cast IN-738LC alloys were imposed as load conditions on structural calculations. It can be clearly seen that at constant heat input, the level of plastic strains and the level of residual (Mises) stresses increase with welding speed. The model predicts highest residual stresses in regions of highest elastic strains, in agreement with conventional phenomenological material models where the macroscopic residual stress is always directly related to the macroscopic elastic strain. The highest residual stresses are located at the fusion line (where coarser dendrite secondary arm spacing exist); and the highest plastic strains are located at centerline (where finer dendrite secondary arm spacing exist). The calculations were performed using ABAQUS ® FE code on the basis of a time-increment Lagrangian formulation.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.524

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 integrity0.0000.001
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 head0.006
GPT teacher head0.199
Teacher spread0.193 · 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

Citations15
Published2010
Admission routes2
Has abstractyes

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