High Pressure Water-Jet Technology for the Surface Treatment of Al-Si Alloys and Repercussion on Tribological Properties
Bibliographic record
Abstract
Recent developments in high pressure water-jet technology have brought the process to the forefront as a means of surface treatment. Water jet technology offers cleaning, cutting, processing as well as potential refinement of surface properties. By adapting the process parameters the surface characteristics can be changed while the profile remains the same. In the present study, water-jet technology was used for the surface treatment of Al-Si alloy to investigate its effect on tribological properties. Dry sliding wear behavior was investigated against AISI 52100 bearing steel ball using a reciprocating ball-on-flat configuration. Optical microscopy examination reveals that ploughing of grains, transgranular and intergranular propagation of cracks; are the mechanisms by which material is removed during water jet treatment. While, on the other hand, SEM observation of the wear track reveals that plastic deformation and delamination are the dominant wear mechanism during the wear process. Water jet treatment was compared to hot isostatic pressing in terms of its effects on wear resistance and surface porosity of Al-Si alloy. It was found that, hot isostatic pressing reduces the total amount of porosity at the expanse of hardness while water jet treatment produces a compressed surface having higher hardness and compressive residual stress, which ultimately increases wear resistance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".