Enhancing Tribological Properties of Metallic Sliding Surfaces through Micro Multi-texturing Techniques
Bibliographic record
Abstract
Friction reduction is important for minimizing energy loss and improving the life of sliding components. Surface texturing is considered an effective way to control the wear and friction on these components. In this research, textured surfaces were created on aluminium workpieces using the tilted micro end milling technique. A flat end mill was used to generate asymmetric dimples. A different series of symmetric dimpled surfaces were also machined using a single crystal diamond cutter. Cutting forces were modelled and compared with the experimental results. On the symmetric dimpled surface, a multi-scale texture process was carried out on the dimples to create a smaller scale roughness through a High-Velocity Abrasive Machining process. A reciprocating tribometer, based on a piezoelectric table dynamometer and a hemispherical ruby counter surface, was used to evaluate friction coefficients under both dry and lubricated sliding conditions. Asymmetric dimples exhibited directional friction effects. For multi-scale textured surfaces, it has been observed a greater reduction in the friction coefficient under lubricated conditions when compared with symmetrical dimples. To gain insight into the mechanism of friction reduction for these surfaces, a series of 2D simulations were performed. These simulations showed that the mechanism of friction reduction is attributed to the ability of dimples to increase the pressure of the lubricant in the contact region resulting from the fluid flow between the sliding surfaces. Moreover, a substantial decrease in the depth of the dimples on worn surfaces was observed, suggesting that entrapment of wear particles within the surface texture features may also influence the measured friction coefficient. Analysis of the wear track depth showed that surface texturing also has a beneficial influence on the calculated Archard wear coefficient.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".