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Record W2558510597 · doi:10.1115/pvp2016-63285

Modelling of a Stud Arc Welding Joint for Temperature Field, Microstructure Evolution and Residual Stress

2016· article· en· W2558510597 on OpenAlexaff
Hadi Soltanzadeh, Jörg Hildebrand, Matthias Kraus, Mahyar Asadi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsUniversity of Ottawa
FundersJohannes Hübner Stiftung
KeywordsResidual stressWeldingMaterials scienceMicrostructureStress (linguistics)Joint (building)AusteniteStructural engineeringWork (physics)Mechanical engineeringThermalMetallurgyEngineeringThermodynamics

Abstract

fetched live from OpenAlex

This paper presents a modelling study and analysis performed on a stud welding including thermal, microstructure and stress calculation. The main concern of this work is toward controlling undesirable residual stresses and the evolution of material properties, as well as the chance of estimating cracks especially with regard to future services of structures. Historically, prediction of welding features is being pursued by welding engineers to enable them for optimal design and mitigation of adverse effects. Stud welding is among the welding processes that are not often addressed by means of modelling and associated activities to develop a comprehensive valid prediction. The aim of this research is to present a modelling practice for a stud weld joint to capture the transient thermal profile, consequent evolution of microstructural phase fractions, and stress calculation using a thermomechanical model based on FE methods (SYSWELD package). The material properties are fed into the model as temperature dependent. The microstructure model is based on t8/5 cooling trajectory on CCT diagram that captures transformation from Austenite phase, and the residual stress calculation is compared to experimental measurement for the sake of validation.

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 score0.218
Threshold uncertainty score0.299

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 head0.015
GPT teacher head0.215
Teacher spread0.200 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

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