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Record W2025600361 · doi:10.1115/1.4005186

The Influence of Mechanical and Laser Cutting on the Fatigue Strengths of Square Hollow-Section Welded T-Joints

2012· article· en· W2025600361 on OpenAlexafffund
Reza Moazed, Reza Fotouhi

Bibliographic record

VenueJournal of Offshore Mechanics and Arctic Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStrain gaugeFinite element methodMaterials scienceJoint (building)WeldingStructural engineeringLaser cuttingSection (typography)Composite materialSquare (algebra)LaserFatigue testingEngineeringComputer scienceMathematicsOptics

Abstract

fetched live from OpenAlex

T-joint connections are commonly encountered in many machine components and load carrying members. The T-joint connections can be fabricated using traditional cutting (machine or flame) or by laser cutting techniques. The present study investigates the feasibility of using laser cutting to produce welded square hollow-section T-joints with similar or higher fatigue strengths than their conventional mechanical cut counterparts. A total of 21 full-scale T-joint samples, typical of those found in the agricultural industry, were included in this study. Nineteen of these samples were examined with the intention of forming a fatigue crack of approximately 3.8 cm (1.5 in.es) in length, and two samples with strain gauges attached for strain measurements. The experimental results of the full-scale T-joints subjected to cyclic loads showed that the fatigue strength of the samples that were manufactured with laser cutting were higher than those fabricated with mechanical cutting. A finite element analysis (FEA) of the T-joints was also performed, and the FEA results were verified with the experimental strain measurements.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.217
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

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 designSimulation or modeling
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

Citations3
Published2012
Admission routes2
Has abstractyes

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Same venueJournal of Offshore Mechanics and Arctic EngineeringSame topicFatigue and fracture mechanicsFrench-language works237,207