Finite Element Analysis of Stress Relief in Teeth of Spur Gear by Varying Key Shaft’s Size and Location
Bibliographic record
Abstract
This paper investigated the Finite Element Analysis (FEA) via ANSYS software of bending stresses in involute gears with different keys shape.It has described the use of ANSYS for predicting the effect of keys on stress distribution in spur gear.The numbers and location of keys used as parameters to observed its effect on the relief stress by changing of key sizes in spur gear.Two modules are used (m=2 and m=7) to tests the spur gear each with one, two, three and four keys position inclusive parallel key (rectangular cross section), parallel key (square cross section) and the other type is parallel key (circular cross section).This analysis of stress was carried out by observing the key factors that determine its relief, the introduction of sizing, location and number of the stress-relieving features at a certain place reaching to relieve maximum in stress performance, otherwise the strength decreases.Using single circular key as a stress reliving feature gives more stress reduction for m=2 and double circular key within m=7.The results for pitch circle diameter=50 mm, outside diameter=54 mm, root diameter=45 mm, center diameter=47 mm, fillet=0.4mm, thickness=3.14mm, and for Case-1-, when B=8 mm and h=7 mm, the maximum equivalent stress is 9635 MPa.In addition, when B=8 mm and h=8 mm, the maximum equivalent stress is 10081 MPa.Also, when the diameter of key=8 mm, the maximum equivalent stress is 9303 MPa.This paper revealed that minimum stresses, therefore, optimum key shaft for spur gears are single circular key for module 2 and double circular key for module 7, and that key geometry, size and number also determine the efficiency of the gears.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".