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Record W3128653773 · doi:10.5781/jwj.2021.39.1.10

Determination of Optimal Weld Parameter for Joining Titanium Alloys by Gas Tungsten Arc Welding using Taguchi Method

2021· article· en· W3128653773 on OpenAlexaff
Nazmul Huda, Jae-Won Kim, Changwook Ji, Dae-Geun Nam, Yeong-Do Park

Bibliographic record

VenueJournal of Welding and Joining · 2021
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsUniversity of Waterloo
FundersDong-Eui University
KeywordsTaguchi methodsWeldingMetallurgyMaterials scienceGas tungsten arc weldingTungstenTitanium alloyArc (geometry)TitaniumArc weldingComposite materialMechanical engineeringAlloyEngineering

Abstract

fetched live from OpenAlex

The optimal parameters for joining two different titanium alloys were determined by the Taguchi method and applied in similar and dissimilar joining of conventional Ti-6Al-4V and newly developed Ti-3Al-2.5V alloys. The microstructures of the two alloys and their mechanical properties were comparatively evaluated at the optimal parameters. The Ti-6Al-4V alloy showed a larger back bead width than that of Ti-3Al-2.5V under similar heat input, because of its lower thermal conductivity and higher specific heat capacity. The welded zone of Ti-3Al-2.5V contained a retained beta phase, which was absent in that of Ti-6Al-4V. This indicates that the transformation of the Ti-3Al-2.5V weld metal starts above the martensite temperature, while it starts below the martensite temperature for Ti-6Al-4V. The failure of the welded specimen occurred in the base metal for both the titanium alloys, which indicates the superior weld quality. However, the welded Ti-6Al-4V showed superior tensile strength to that of the Ti-3Al-2.5V weld under optimal conditions, owing the high beta phase fraction in its base metal. Meanwhile, it showed inferior ductility to that of Ti-3Al-2.5V because of its coarser beta phase.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.632

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 head0.025
GPT teacher head0.292
Teacher spread0.267 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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