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Record W4405112806 · doi:10.1115/1.4067374

Insights on the Tribological Characteristics of Titanium Alloys in Demanding Environments

2024· article· en· W4405112806 on OpenAlexafffund
Francisco Rivadeneira, Payank Patel, Agnieszka M. Wusatowska-Sarnek, Mary Makowiec, Pantcho Stoyanov

Bibliographic record

VenueJournal of Tribology · 2024
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsCollège de MaisonneuveConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTribologyMaterials scienceTribometerAbrasion (mechanical)DurabilityReciprocating motionCorrosionMetallurgyOxideLayer (electronics)Titanium alloyTitaniumComposite materialAlloyMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Titanium alloys are widely used in demanding applications due to their exceptional strength-to-weight ratio, high-temperature resilience, and excellent corrosion resistance. Understanding their tribological behavior is critical, as the performance and durability of several mechanical systems, particularly in gas turbine engines, are often constrained by friction and wear in complex contacting and mobile assemblies. This study investigates the tribological behavior of two widely used titanium alloys, Ti–6–4 and Ti–6–2–4–2, focusing on their interfacial phenomena under varied operational conditions. Tribological testing was conducted using a reciprocating tribometer at different temperatures and loading conditions. Ex situ analyses revealed that wear mechanisms were heavily influenced by the properties of the oxide layer formed during sliding. Under higher loads, the oxide layer on the alloy surface fractured, resulting in the generation of flake-like debris, which contributed to third-body abrasion. Additionally, the study examined the transfer film formation on the alumina counterface under various conditions, correlating friction, and wear behavior with interfacial processes, particularly the oxide formation on the worn surfaces. This study enhances the understanding of the tribological behavior of titanium alloys, paving the way for improved performance in demanding applications through advanced surface modification techniques.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.460
Threshold uncertainty score0.172

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 head0.020
GPT teacher head0.230
Teacher spread0.210 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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