Selection and Characterization of a Flexible Seal to Allow Sheet Flow during Superplastic Forming
Bibliographic record
Abstract
The auto industry aims to deliver cost-effective, efficient vehicles to meet customer needs. They are utilizing aluminum to lower expenses, enhance durability, and lighten vehicles. Currently, the industry is developing a high-speed blow forming (HSBF) technique—a faster version of the aluminum thermoforming process, superplastic forming (SPF). HSBF allows the rapid creation of aluminum bodywork or structural parts at high temperatures using pressurized gas. It can produce up to 25 parts per hour, significantly faster than SPF, which only produces 4 parts per hour. The primary objective of this project is to select and characterize a seal that can increase the production rate to 120 parts per hour by allowing the sheet to flow into the mold, especially during the initial stages of the forming process, where most of the deformation occurs. Several test benches were developed to assess the performance and durability of the selected high-temperature seals under conditions that imitate the HSBF process. During the tests, low air pressures are applied to a gasket-enclosed cavity and the resulting mass-flow leakage is measured. The temperature of the mold is kept constant at approximately the superplastic temperature of the aluminum alloy. Through testing, we derived leakage mass flow curves based on cycle count, showcasing the superior sealing ability and longevity of packing seals in HSBF conditions. The seals displayed good durability and sealing performance under HSBF operational conditions, sustaining over 3000 cycles. Moreover, the seals attained a leakage mass flow rate of around 0.3 g/s·m·bar, nearly ten times below the target application limit of 2 g/s·m·bar, confirming their superior performance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".