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Record W4396954748 · doi:10.1016/j.wear.2024.205400

Evaluating surface mechanical properties and wear resistance of Ti–6Al–4V alloy subjected to ultrasonic pulsed waterjet peening

2024· article· en· W4396954748 on OpenAlexafffund
P. Siahpour, S. M. T. Omar, D. Griffin, Mark Yao Amegadzie, A. Kiet Tieu, I.W. Donaldson, Kevin P. Plucknett

Bibliographic record

VenueWear · 2024
Typearticle
Languageen
FieldEngineering
TopicSurface Treatment and Residual Stress
Canadian institutionsCurrent Water Technologies (Canada)Dalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials sciencePeeningTraverseSurface roughnessMetallurgyScratchReciprocating motionAlloyHardnessShot peeningSevere plastic deformationSurface finishComposite materialResidual stressMechanical engineering

Abstract

fetched live from OpenAlex

The alloy Ti-6Al-4V is widely utilized in various industrial applications, yet its inherent susceptibility to mechanical wear and friction has led to performance limitations. Addressing this, surface modification techniques have been employed to improve material surface properties. Among these, ultrasonic pulsed waterjet (UPWJ) peening emerges as a viable solution due to its ability to improve surface characteristics without causing excessive plastic deformation, unwanted thermal effects, or surface contamination. This study is focused on the influence of UPWJ peening parameters, particularly traverse speeds ranging from 200 to 1000 mm/s, on wrought Ti-6Al-4V (grade 5). Comprehensive characterization encompassed surface roughness, scratch hardness, and reciprocating wear analysis. Post-test examinations, including FE-SEM, CLSM, and EDS were employed to provide detailed microstructural and elemental insights. The results revealed that traverse speeds between 800-1000 mm/s yielded a remarkable 55 % enhancement in scratch hardness. Additionally, wear behavior exhibited a correlation with UPWJ traverse speed; notably, a speed of 900 mm/s indicated a 12 % improvement in wear resistance under a 40 N load. This research highlights the interplay relationship between UPWJ peening parameters and Ti-6Al-4V's wear performance, contributing to its broader understanding and application potential.

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 score0.015
Threshold uncertainty score0.571

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 head0.042
GPT teacher head0.268
Teacher spread0.226 · 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

Citations27
Published2024
Admission routes2
Has abstractyes

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