Evolution of Lubrication Characteristics of Double-Nut Ball Screws Based on an Efficient Surface Roughness Modeling Method
Bibliographic record
Abstract
Abstract Accurate prediction of lubrication characteristics, specifically film thickness and pressure distribution, is pivotal for ensuring optimal performance in ball screws. Despite its significance, there is a notable dearth of studies investigating the evolution of lubrication characteristics resulting from the changing surface roughness over prolonged operation of ball screws. Furthermore, obtaining the surface roughness of ball screws poses a challenge due to the limited loading capacity of measurement instruments. Traditional methods involving cutting the screw for surface roughness measurement are impractical for continuous monitoring during extended operation. To address this issue, the present study introduces an efficient approach to model the surface roughness of the raceway in double-nut ball screws. A profilometer is employed to measure profile roughness along two directions (parallel and perpendicular to the rolling direction) without the need to cut the screw raceway. The 2D power spectral densities and height probability densities of profile roughness are calculated to model the surface roughness, and the synthesized data are utilized to solve the Reynolds equation. The simulation method is validated through friction torque tests, demonstrating a calculation accuracy exceeding 92%. The study further explores the evolution of film thickness and pressure distribution in double-nut ball screws during running-in and steady wear stages, revealing severe asperity contact in the two nuts. Additionally, variations in load ratio, friction coefficient, and film thickness ratio (λ) are investigated. Considering the load ratio and λ of the slave nut, it can be inferred that boundary lubrication persists in the two nuts throughout the operation.
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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 | 0.000 | 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".