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Record W2804750031 · doi:10.5772/intechopen.72394

Numerical Simulation of Residual Stresses in Welding and Ultrasonic Impact Treatment Process

2018· book-chapter· en· W2804750031 on OpenAlexaff
Lanqing Tang, Ayhan Ince, Jing Zheng

Bibliographic record

VenueInTech eBooks · 2018
Typebook-chapter
Languageen
FieldEngineering
TopicSurface Treatment and Residual Stress
Canadian institutionsConcordia University
Fundersnot available
KeywordsResidual stressWeldingMaterials scienceResidualProcess (computing)Ultrasonic sensorStructural engineeringComposite materialAcousticsEngineeringComputer scienceAlgorithmPhysics

Abstract

fetched live from OpenAlex

Welding technology is considered as a reliable and efficient joining method, which has been widely used in almost all the industry departments. Detrimental factors induced by welding such as micro-cracks/flaws, tensile residual stresses, high stress concentration may degrade the mechanical and fatigue properties of weld joints. Ultrasonic impact treatment (UIT) is considered one of the most efficient post-weld treatment which could improve the fatigue performance of weld joints. In this study, the effect of the UIT on residual stress distribution of 304L weld joints was particularly investigated. FE analysis simulation and the XRD experiment were performed to predict and measure residual stresses of both as-welded and the UIT-treated joints. Compared results show that simulated stresses are in good agreement with the experimental results along various paths, confirming the validity of welding model. The UIT introduces a compressive residual stress layer with depth between 2 and 3 mm near the impacting surface of weld joint.

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 categoriesMeta-epidemiology (narrow)
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 score0.936
Threshold uncertainty score1.000

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 head0.022
GPT teacher head0.287
Teacher spread0.265 · 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.

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

Citations2
Published2018
Admission routes1
Has abstractyes

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