A New Two-Stage Degradation Model for the Preload of Linear Motion Ball Guide Considering Machining Errors
Bibliographic record
Abstract
Abstract Preload, which is widely applied in linear motion ball guide (LMBG) to eliminate clearance and increase stiffness, gradually decreases owing to wear, resulting in the degradation of the load-bearing capability and dynamic response of LMBG. However, no solution can be found on the modeling of the preload degradation of LMBG considering raceway profile error, ball diameter error, etc. Therefore, this paper presented a new two-stage degradation model to predict the preload variation of LMBG with considering machining errors. The model was experimentally verified with the prediction accuracy much higher than the model considering no machining errors at either of the two wear stages, which demonstrates the effectiveness of considering machining errors. Additionally, the effects of waviness errors, external load, and feed speed on the preload degradation of LMBG were discussed. The simulation results indicate that the preload loss rate rises with the increase of waviness error, external load and feed speed. For obtaining a longer effective service life of LMBG, it is helpful to select appropriate external load and feed speed conditions and improve the processing technique as well as the machined surface quality.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".