Numerical and Experimental Study of Tube Hydroforming for Aerospace Applications
Bibliographic record
Abstract
In the tube hydroforming (THF) process, a pressurized fluid is used to expand a thin walled tube inside a closed die in order to fill the die cavity. THF has many advantages that render this process interesting for different industries such as automotive and aerospace. In this work, to investigate the effect of different process parameters, such as the friction condition, tube thickness and end-feeding on the final product, THF experiments were performed on stainless steel 321 (SS 321) tubes using a round-to-square die. Experimental loading paths were obtained via the data acquisition system of the hydroforming press, which is fully instrumented. An automated deformation measurement system, Argus®, was used to measure the strains on the hydroformed tubes. The THF process was simulated using Ls-Dyna software. The variation in the strain and thickness measured from the experiments were compared to the simulation results at critical sections. Comparison of the results from the finite element (FE) simulations and experiments showed good agreement, indicating that the approach can be used for predicting the final shape and thickness variations of the hydroformed parts for more complex shapes in aerospace 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".