Effect of Surface Texturing on the Steady-State Properties and Dynamic Coefficients of a Plain Journal Bearing: Experimental Study
Bibliographic record
Abstract
The last decade has seen a significant and increased interest in surface texturing technologies esulting in improving the overall tribological properties of mechanical components. Several techniques could be used to modify the surface topography with laser surface texturing becoming most popular recently. In this investigation, rig experiments have been performed on plain and textured-surface journal bearings with an aspect ratio of 1.1 under a variety of loads and speeds. Percussive burnishing (embossing) was used to create the dimples on the internal surfaces of the test bearings. The dimples have a spherical shape with a diameter of 1 mm and a depth of 60 micrometers. Pit-area ratio was in the range 5–20% of the total bearings surfaces. The effects of surface-texturing and dimple density on the bearing steady-state characteristics and dynamic coefficients are analyzed and discussed in this paper.
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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".