Simulation of shear spinning process using finite element method
Bibliographic record
Abstract
Spinning process for the medium- and small-lot production of axisymmetric components has many advantages, such as smaller deformation force, simpler tool-and-die design, and lower investment in equipment, over conventional forming processes. This paper presents a 3-D elastoplastic finite element (FE) model for the simulation of the shear spinning process, in order to study deformation behaviour and design proper process parameters for the spinning process. The proposed model has the following characteristics: i) a shell element is used to mesh the contact pairs between the workpiece (a metal blank) and the tool sets (a pressing roller and a mandrel); ii) the offset thickness of the shell element is considered during contact treatment; iii) the movement of the roller and the fixing of the central blank and mandrel are treated as the boundary condition; iv) relative movement between the roller and the blank is treated as a spiral feeding process. FEM simulations for shear spinning with aluminum have been implemented, using a dynamic explicit scheme, on ANSYS/LS-DYNA software. Spinning force, thickness distribution and stress distribution have been studied under various roller feeds and inclined angles of mandrel. The simulation results have provided good confirmation with the experiments.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".