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Record W2484347493 · doi:10.1080/13621718.2016.1212971

Microstructure and fatigue properties of linear friction welded TC4 titanium alloy joints

2016· article· en· W2484347493 on OpenAlexaff
Shiqing Wang, T. J., Wenya Li, Guodong Wen, D.L. Chen

Bibliographic record

VenueScience and Technology of Welding & Joining · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsToronto Metropolitan University
FundersNational Natural Science Foundation of China
KeywordsMaterials scienceWeldingTitanium alloyCyclic stressJoint (building)MicrostructureMetallurgyComposite materialSofteningDeformation (meteorology)AlloyBase metalHysteresisFatigue limitStructural engineering

Abstract

fetched live from OpenAlex

The welded joints are often subjected to cyclic stresses and high-strain-rate deformation resulting in worrisome fatigue failure. Therefore, it is necessary to evaluate the fatigue resistance and cyclic deformation characteristics of these weldments. Ti–6Al–4V–0.3Fe–0.1C–0.05N–0.015H–0.2O (TC4) titanium alloy joints were produced via linear friction welding and a sound joint was obtained. It was observed that fine subgrains formed in the weld zone where the hardness became higher. The strain ratio had a strong effect on the cyclic deformation characteristics of the joint, with hysteresis loops being different at different strain ratios. However, the difference of fatigue life of the joint was small with varying strain ratios. The stress amplitude of linear friction welded TC4 joint showed essentially cyclic softening until failure at all strain ratios. Fatigue cracks initiated from the near-surface of base metal and propagated by the formation of fatigue striations together with secondary cracks.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.232
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), 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

Citations18
Published2016
Admission routes1
Has abstractyes

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