Deterministic Design for Load Capacity Enhancement in Planetary Roller Screw Mechanism With Different Thread Profiles
Bibliographic record
Abstract
Abstract Load distribution in the planetary roller screw mechanism (PRSM) is typically non-uniform, leading to extremely high contact pressure and shortening the service life. This study presented a deterministic design method to address the inherent non-uniformity of load distribution. By the matching design of screw, roller, and nut pitch, the deformation coordination that causes uneven load distribution was effectively compensated. The structural parameters and thread profile of PRSM were further optimized to enhance load capacity. The effects of installation configurations, structural parameters, and thread profiles on the load capacity performance of PRSM were comprehensively explored. A larger nominal diameter ratio of roller to screw reduces the maximum contact pressure on the nut–roller interface consistently, while the pressure on the screw–roller interface decreases initially and then increases, with an about 5/8 ratio offering the optimal balance for load capacity performance across both interfaces. A smaller pitch leads to a more uniform load distribution and a reduction in the maximum contact pressure. Additionally, convex–concave–concave thread profiles for screw, roller, and nut effectively minimize the maximum contact pressure on both interfaces. This study provides a highly effective and innovative tool for the structural design of PRSM with high load capacity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".