Perspectives for the Application of Variable Thickness Aluminium Tubes in Hydroforming of Complex Tubes
Bibliographic record
Abstract
Tubular products have very important applications in various areas especially in the transportation industries. For instance, in the structure of cars there are various tubular products like roof headers, engine cradles, roof rails and frame rails with complex geometries which most of them need multiple steps like tube drawing, tube bending and hydroforming for their production. Based on the recent studies by this group, it was proven that in most of the structural tubular parts in the cars it was not necessary to have constant thickness along the axial direction of tube and it will be considered as overdesign and the overall weight of structures can be reduced considerably by using variable thickness tubes. In this paper, the variable thickness tube drawing and its applications in the tube bending and hydroforming applications were studied. The results showed that this process can have important role in reduction of defective parts in the production of complex tubes by the tube hydroforming method. However especial considerations should be taken into account in the design of thickness distribution along axial direction of these kinds of tubes to avoid problems in the drawing step and as well in the bending and hydroforming steps.
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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".