Finite element analysis and optimization of a mono parabolic leaf spring using CAE software
Bibliographic record
Abstract
Parabolic leaf spring is one of the vital components in vehicle suspension system, and it is commonly used in heavy vehicles.It needs to have an excellent static load bearing capacity and fatigue life too.The purpose of this work is to make computer aided engineering (CAE) analysis of mono parabolic leaf spring and to see the effect of change of material in the optimized leaf.A mono steel leaf spring and a mono leaf spring made of composite material have been selected for this comparative analysis.The material of the mono steel leaf spring is EN45A and Glass Reinforced Plastic (GRP) as composite material which is having high strength to weight ratio.The mono leaf spring model is having one full length leave with eyes at both ends, two pins in each eye end and a rubber pad on the upper face of leave center.The CAD modelling of parabolic leaf spring has been done in CATIA and for analysis the model is imported in ANSYS workbench.It was shown that the use of composite material instead of steel resulted into large deflection, small variation in stresses and also a large amount of weight reduction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".