Characterization and FEA Based Optimization of Elastomeric Components for Automotive Applications
Bibliographic record
Abstract
This study aims to show the implementation of an optimization procedure by characterizing the material’s hyperelastic behavior in harsh environments experienced in facility testing and in real world automotive conditions. The procedure consists of conducting material tests under various simulated harsh environments, determining the co-efficients for hyperelastic material modeling and using FEA to predict and correlate the nature of failure observed in facility testing. Two commercially available and commonly employed thermoplastic elastomers (TPE), Santoprene (Ethylene-Propylene-Diene-Monomer rubber and Polypropylene blend) and Desmopan (Thermoplastic Polyurethane), were tested. The harsh environments simulated are fluid immersion tests in automobile grease. Material aging characteristics in controlled thermal conditions were also documented. Compression and tension tests were conducted in order to determine the co-oefficients of the Mooney Rivlin hyperelastic material model. Finite Element Analysis (FEA) simulations were conducted on LS-DYNA software, to determine the quasi-static stress distributions on an overslam bumper part, a typical application of automotive elastomers. Shape and topological variations were investigated in the FEA tests. It was found that certain shape and topological changes to the part result in minimizing the stress concentrations. It is hypothesized that such changes to the rubber component would result in a lower failure rate in facility testing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".