MicroSurface Texturing for a Minimum Coefficient of Friction
Bibliographic record
Abstract
In recent years, micro surface texturing for friction and adhesion control has gained momentum in a wide range of applications, such as MEMS devices, punches, and tools used metal forming processes, and injection molding machines. In this study, air hardened tool steel, A2, with micro hexagonal dimples of different sizes and densities but constant depth, have been modeled and tested under dry sliding contact. Three-dimensional finite element models depict sliding dry contact between a rigid indenter and elastic-plastic textured surfaces are simulated. Coefficients of friction have been determined and compared for different texturing sizes and densities. In addition, these hexagonal patterns were fabricated on tool steel (A2) samples using photolithography. Coefficients of friction were experimentally measured using micro scratch tribometer. Both simulation and experimental results show there is a strong correlation between micro-texturing parameters and coefficient of friction. The results demonstrate that under dry sliding contact, coefficient of friction can be controlled through optimization of micro texturing parameters, specifically the spatial texture density (D/L) which is equal to the ratio of the size of the dimple (D) to the distance between the centers of two consecutive dimples (L). A minimum coefficient of friction exits at values of spatial texture densities (D/L) that range between 0.25 and 0.5 for this specific material.
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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) | 0.000 | 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) | 0.000 | 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".