Dynamic theory and experimental testing in ipsilateral offset dual-motor excitation system
Bibliographic record
Abstract
This paper addresses unbalanced trajectories of dual-motor vibrating screens by proposing a dynamic theory and experimental testing. First, based on the Lagrange equation, the dynamic model of ipsilateral offset dual-motor excitation system is established. Subsequently, the steady response is worked out by solving the differential equation of motion of the vibrating body. Then, the synchronization condition and synchronization stability criterion of the system are determined employing the small parameter method and the Routh–Hurwitz criterion. Numerical analysis methods are given to reveal the relationship between structural parameters and synchronization characteristics of the vibration system. Moreover, an mechanical–electric coupling model is developed to study dynamic characteristics. The results demonstrate that synchronization condition can be described with capture torque and difference of output torque between motors. The synchronization ability is enhanced by improving installation distance of the motors. Additionally, increasing angle β or decreasing angle θ of the motors is in favor of optimization of centroid trajectory; swing oscillation of the box can be controlled by adjusting the main inclination angle of motors; in such a condition, equilibrium of motion trajectory of system is enhanced. Finally, the validity method for designing vibrating screen is verified through theoretical calculations, simulation analysis, and experiments.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".