Evaluating surface mechanical properties and wear resistance of Ti–6Al–4V alloy subjected to ultrasonic pulsed waterjet peening
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".