Dynamic Characteristic Analysis of Spur Gear System Considering Tooth Contact State Caused by Shaft Misalignment
Bibliographic record
Abstract
Abstract Shaft misalignment will change the gear contact state, and then leads to the variation of the internal stiffness excitation of the gear pair, and finally the dynamic characteristics of the gear system will be affected. However, the influence of the gear contact state change on stiffness is usually neglected in the traditional stiffness calculation model for misaligned gears, and the underlying influence mechanism of the gear contact state changes aroused by the shaft misalignment on the dynamic characteristics of gear system is still unclear. To address these shortcomings, traditional loaded tooth contact analysis (LTCA) model is improved with the influences of fillet foundation deformation taken into consideration. Combined with the improved LTCA model, a new mesh stiffness calculation model for misaligned gear considering the tooth contact state is proposed, and then the effects of the contact state changes aroused by the shaft misalignment on the mesh stiffness excitation are studied. Moreover, a dynamic model of misaligned gear system with 8 degree of freedom (DOF) is established, and the dynamic characteristics of the system are simulated and finally verified by experiment. The results show that the proposed model can be used to evaluate the dynamic characteristics of the misaligned gear system with the change of gear tooth contact state taken into consideration. This study provides a theoretical method for the evaluation and identification of the shaft misalignment error.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".