Contact Stress Analysis and Optimization of Bevel Gear Pairs by Theoretical and FEA
Bibliographic record
Abstract
The gears are used to transmit the motion and power from one shaft to another shaft by physical contact between gear teeth. The bevel gear is used to transmit motion and power between the axis of intersection and the non-intersecting axis. Gears are generally subjected to loads due to these loads, tooth bending stress and contact stress will be developed on the gear tooth. Much research on the action of the gear has confirmed that contact stresses also influence the formation of pits on the surface of the tooth. Gear tooth is the most important element in a gear system and has been focused on the current study. An analysis has been carried out for the three different types of materials used to make the bevel gear. The failure of gears due to contact stress is high compared to bending stress. Stress analysis has been a key area of research to minimize failure of the gear and optimize the design. The study of contact stress developed between the mating gears are very important for the gear design. The current goal is that the finite element analysis of the bevel gear is performed to determine the maximum contact stress by ABAQUS as the solver and also the theoretical maximum contact stress is calculated by the Hertz equation. In this study finite element analysis results were validated with theoretical results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".