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Record W3178527183 · doi:10.18280/rcma.310306

Performance Assessment of the Developed Flux Powder on the Tensile and Hardness Properties of Steels Joints Using TIG-Welding

2021· article· en· W3178527183 on OpenAlexvenueno aff
Sunday A. Afolalu, Omolayo M. Ikumapayi, Temitayo S. Ogedengbe, M.E. Emetere

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2021
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsnot available
FundersCovenant University
KeywordsMaterials scienceWeldingGalvanizationMetallurgyGas tungsten arc weldingUltimate tensile strengthFlux (metallurgy)Tensile testingIndentation hardnessHeat-affected zoneComposite materialMicrostructureArc welding

Abstract

fetched live from OpenAlex

The conversion of waste to wealth has recently grossed high attention as it possesses the ability to boost the economy of any nation; hence, this research. In this study, the characterization and investigation of mechanical properties of nano- flux (CaO) welding powder developed from bio-agrowaste (eggshell) was carried out. Mild, galvanized and stainless steel of plates and rods were used as parent metals for the experiment. Results obtained from hardness test in the base metal, weld joint and heat affected zone for the galvanized and mild steel plates with nano-flux powder gave the best hardness of 111.95, 120.30, 182.99 and 206.21, 164.85, 110.56 BHN respectively. The tensile stress obtained both for mild and stainless steel was 88.14 MPa while the tensile strain obtained for both plates were 0.0155 mm/mm. Microstructural analysis results shows an improvement in the structure, surface and patterns of the weld with the use of developed flux compared with imported flux. Hence eggshells can be recycled and used for developing flux powder for welding processes.

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 score0.124
Threshold uncertainty score0.395

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 head0.109
GPT teacher head0.279
Teacher spread0.169 · 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
Published2021
Admission routes1
Has abstractyes

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