Hybrid Modeling of Ball Screw Drives With Coupled Axial, Torsional, and Lateral Dynamics
Bibliographic record
Abstract
It has been a common practice to assume that the torsional and axial dynamics are totally decoupled from the lateral dynamics of the screw when modeling ball screw drives. However, experiments show that there is a considerable coupling between them, which could adversely affect the positioning accuracy and fatigue life of the drive. In this paper, the lateral dynamics of the screw is explicitly incorporated into the hybrid finite element model of ball screw drives. The ball screw is modeled by Timoshenko beam elements, and the balls, joints, bearings, and fasteners are modeled as pure springs. Rigid components are modeled as lumped masses. The proposed screw-nut interface model, which includes the effects of lateral vibrations, is shown to predict the coupling between axial, torsional, and lateral dynamics of ball screw drives. The effects of this dynamic coupling on the positioning accuracy of the drive are also presented with experimental proof. The proposed model provides a more realistic platform for a designer to optimize the drive parameters for high speed-high acceleration machine tool applications, where the ball screw vibrations limit the fatigue life of the mechanism, bandwidth of the servo systems, and positioning accuracy of the machine.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".