Investigation of the precision loss for ball screw raceway based on the modified Archard theory
Bibliographic record
Abstract
Purpose This paper aims to examine the precision loss of ball screw raceway under different operating conditions and geometry parameters. Design/methodology/approach Based on a new coefficient K’ introduced especially for ball screws to reflect the actual contact condition, the modified Archard theory is applied to ball screws to obtain wear volume of the ball-screw contacts. Thus, the axial precision loss can be defined as the ratio of the wear volume to the contact area. Meanwhile, a novel running bench and a precision-measuring system of ball screws are conducted. Precision variation is obtained and analyzed during the whole life running test, which agrees well with the theoretical values calculated in this paper. Findings For a given rotational speed, the increasing rate of the precision loss rate is high at low axial load and then becomes small with the increasing axial load, whereas for a given axial load, the precision loss rate is proportional to the rotational speed. Besides, the precision loss rate is reduced with the increasing contact angle between a ball and the screw raceway, and is proportional to the helix angle when the angle changes from 1 to 10 degrees. Research limitations/implications The rotational speed used in this experiment is low and the ball screw is of no-load type, although results calculated by the model and Wei’s model seem close when the axial load is high, whether the model built in the paper is applicable to the condition of high rotational speed and preload still needs to be verified in the future work. Practical implications This study provides an accurate model to predict the precision loss of the screw raceway and estimate the remaining life of ball screws, which is significant for better performance of ball screws as well as the computer numerical control machine tools. Originality/value Previous studies on the wear of ball screws mainly focused on the drag torque analysis and mechanical efficiency estimation, and the experiment to verify their theoretical analysis was almost all limited to the test of drag torque or axial rigidity, which is neither sufficient nor persuasive. However, in this paper, the authors proposed a comprehensive wear prediction model which combines the modified Archard wear theory, Hertz contact theory and kinematic theory of ball screws. To the best of the authors’ knowledge, this kind of study has never been reported in the literature. In addition, for the lack of the test bench and high cost of the experiment, the whole life operation test, which is designed and conducted to confirm the model in this paper, has never been reported in literature either.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".