Finite Element Modeling and Structural Parameter Optimization of Ball Hinge Rubber for Commercial Vehicle Thrust Rod
Bibliographic record
Abstract
When the thrust rod of commercial vehicles is under the ultimate load, rubber extrusion or even cracks may occur in the rubber layer, which affects the operation safety of the vehicle. In order to reduce the failure probability of the thrust rod and extend its service life, this paper simulated the working condition of the thrust rod ball-hinged rubber by finite element method, revealed the relationship between the maximum stress of the thrust rod ball-hinged rubber and its structure, and proposed an optimization scheme. Firstly, based on the stress-strain data of rubber material, the parameters of elastoelastic constitutive model of rubber material were fitted in ABAQUS software. Secondly, the finite element model of ball hinge rubber was established to reduce the maximum working stress of rubber layer by changing the parameters of rubber layer thickness and its edge shape, so as to extend the service life of thrust rod. The results show that reducing the thickness of the rubber layer can effectively reduce the maximum stress of the rubber layer, but the stress concentration is not significantly improved, and the crack spreads to the inner surface of the rubber layer, and changing the edge shape of the rubber layer to convex, not only can greatly reduce the maximum stress, but also effectively alleviate the stress concentration, but the rubber layer edge of the outer surface of the tear. Finally, the optimal combination scheme of the two structural parameters was determined by orthogonal test, which greatly reduced the maximum stress and improved the stress concentration, reduced the crack propagation length, and no tearing occurred on the inner and outer surfaces of the rubber layer. The effectiveness of the orthogonal experiment optimization scheme is proved, which can lay a theoretical foundation for the optimization design of thrust rod ball hinge rubber.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".