CFD Modeling of Elastomeric Silicone Material Dispensing to Enable Equipment Design & Optimization for Efficient Automotive Manufacturing
Bibliographic record
Abstract
In the automotive industry, it is essential to consider not only how well specialty materials perform and are formulated, but also how efficiently and economically they can be applied during manufacturing. This becomes especially important during the early stages of development to prevent issues when these materials are used in new designs by automotive suppliers or manufacturers. With the rapid growth of electric vehicles (EVs), new materials are being used more frequently, and these materials may not have been as thoroughly tested as those used in traditional internal combustion engine (ICE) vehicles. Therefore, it is crucial to ensure that these materials can be applied correctly and efficiently from the start. One way to speed up the development process is through Computational Fluid Dynamics (CFD) modeling. CFD helps predict how materials will behave when dispensed, which is essential for developing the right equipment and conditions for applying these materials. Working with Graco, a leading manufacturer of fluid management and dispense solutions, Dow and Graco have used these models to design better equipment solutions and improve the software used. This ensures high-quality and efficient application at start up. This paper will present two case studies how material models were developed, tested, and validated with real-world data to ensure they work as intended. The goal is to demonstrate how optimizing dispensing equipment based on material characteristics is critical for maintaining process windows and product quality. By doing so, manufacturers can achieve better results, reduce waste, and ensure the reliability of new materials in automotive applications.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".