Static structural CAE analysis of symmetrical 65Si7 leaf springs in automotive vehicles
Bibliographic record
Abstract
Leaf springs in vehicles are used to absorb, store and release energy.During this cycle stresses induced in the springs must not exceed design stress, in order to avoid settling or premature failure.Number of experiments are done in order to determine the stresses, load rate and deflection, which involves lot of time and cost.Today, the technologies in leaf springs are changing gradually; therefore new tools are required to keep aligned with worldwide technological requirements.The work presented in this paper provides a CAE solution to static analysis of 65Si7 leaf springs used in light commercial vehicles (LCV's).A practical model of leaf spring used in LCV has been taken into consideration for this study.It has been experimentally tested for deflection, stress and load rate on a full scale leaf spring testing machine.A static structural CAE analysis of leaf spring has been done under similar loading condition.The CAD model of the leaf spring has been prepared in solid works and analyzed using ANSYS.Using CAE tools, ideal type of contact and meshing elements have been proposed to achieve results closer to the experimental results.The analytical method for static analysis of the leaf springs has also been described.CAE results have been compared with experimental and analytical results for validation.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".