Deflection Prediction of an Anti-vibration Mount by Finite Element Analysis
Bibliographic record
Abstract
To ensure the safety and dependability of rubber components, deflection analysis and prediction play a crucial role in process of design.Material property testing and finite element analysis (FEA) are combined to forecast the maximum deflection of a railway elastomeric pad.IRMRA (Indian Rubber Manufacturer's Research Association) developed the chloroprene rubber.Using the FEA method, maximum deflections of an anti-vibration mount under several compressive loads are calculated.Mooney-Rivlin nonlinear hyperelastic three parameter model with element type Plane 182 is used for and FEA.Curve fitting of the uniaxial tensile test results is used to extract three parameter Mooney-Rivlin model constants by using FEA.Then, these Mooney-Rivlin model constants are used to analyze anti-vibration mount and predict the deflections at different compressive loads.The outcomes are contrasted with the technical specifications provided by the Research Designs and Standards Organization of Indian Railway and predicted deflections are within the limits of maximum values allowed.The results are also contrasted with data from literature, and 10% variation is observed between results obtained and literature results.This methodology can be used to predict deflections of any newly developed rubber at initial stage of design.
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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 | 0.000 | 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".