Analytical Modeling and FE Simulation for Analyzing Applied Forces During Roll Bending Process
Bibliographic record
Abstract
High strength steel is widely used in the manufacturing of parts dealing with heavy cyclic loads and corrosive environments. However, processing this type of steel is not easy, and it becomes a hard-to-solve problem when the part to produce is large, thick and quasi-unique. One example of a thick high strength steel axisymmetric part is the conical shape of the crown of a Francis turbine runner. Some Francis turbine runners installed in the dam basement of a hydraulic power plant are 10 meters in diameter with more than 5 meters in height, while plate thickness can exceed 100 millimeters. Several processes can be envisaged for the manufacturing processes of such large parts (welding or casting…), but few processes can deliver one within a reasonable time and at competitive cost. Among them the roll bending process, causing plastic deformation of a plate around a linear axis with little or no change in plate thickness, is considered as an interesting alternative. The main objective of this research is to assess 3D dynamic finite element and analytical models for the computation of the bending forces during the manufacturing of hollow conical parts made of a thick plate and a high strength steel. Numerous parameters such as thickness, curvature, part size, material properties and friction directly influence the reaction forces on the rolls. Therefore, the results of this research provide a better understanding of the phenomena taking place in the process, and an opportunity to establish relationships between the bending forces and the parameters of a final conical part.
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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 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".