Dynamic FE Analysis for Reducing the Flat Areas of Formed Shapes Obtained by Roll Bending Process
Bibliographic record
Abstract
Roll bending is a continuous forming process where plates, sheets, beams, pipes, and even rolled shapes and extrusions are bent to a desired curvature using forming rolls. Over the years, with the advantages such as reducing setting up time, the cost in tooling investment and equipment, the roll bending process was fundamental for manufacturing cylindrical shapes. However, the process always leaves a flat area along the leading and trailing edges of the workpiece. Therefore, accuracy could be a challenge when the part to be produced is large and made of high strength steel. There are several methods to minimize the flat area. Among them, for the asymmetrical configuration, moving slightly the bottom roll along the rolling direction may have the highest effect. On the other hand local adjustment of the bottom roll location is also important for providing the pressure needed for gripping and carrying the workpiece through the rolls. Then by optimizing the vertical displacement of the bottom roll one can minimize the span of flat areas. The main objective of this research is to assess 3D dynamic Finite Element (FE) model with Ansys/LS-Dyna for the simulation and analysis of the deformation of the workpiece during the manufacturing of cylindrical parts. Various dynamic simulations based on 3D element are performed to provide better understanding of the whole deformation history and to establish the relationship between the location of the bottom roll and the end shapes of the formed cylinders. The results from FE simulations are then compared with corresponding experimental results from an industrial roll bending machine in order to improve the quality of the final shape.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".