A Study of Micro-Dimpled Groove Texture in Friction Reduction
Bibliographic record
Abstract
Abstract Friction control is a vital green technology for reaching sustainable development goals, and surface texturing is effective and efficient at reducing friction. In this study, a novel preparation scheme involving dimpled groove texture is proposed. A pulsed laser marking system is used to make the dimpled grooves. Due to the unique design and process to make the proposed texture, texturing time can be greatly minimized compared to the conventional micro-groove texture technology. A computational fluid dynamics (CFD) model that considers the dimple surface angle is employed to aid the design of the texture and to predict the performance of the texture under certain conditions. Reciprocating sliding tests are preformed to evaluate the effects of the texture on friction reduction. Textures with different densities are used in the experiments. The results suggest that the dimpled groove reduces the coefficient of friction by 6% with less density distribution on the sample surface; nevertheless, denser densities may reverse the result. The simulation results suggest that the texture may perform better under pressure of the lubricant film in hydrodynamic conditions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".