Methods, Models, and Assumptions Used in Finite Element Simulation of a Pilger Metal Forming Process
Bibliographic record
Abstract
The pilger process is a cold-worked mechanical process that combines the elements of extrusion, rolling, and upsetting for the formation of thin-walled tubes. This complex manufacturing process relies on the results of trial and error testing programs, experimental parameter sensitivity studies, and prototypical applications to advance the technology. This finite element modelling effort describes the methods, models, and assumptions used to assess the process parameters used to manufacture thin-walled tubing. The modelling technique breaks down the manufacturing process into smaller computer generated models representing fundamental process functions. Each of these models is linked with the overall process simulation. Simplified assumptions are identified and supporting justifications provided. This work represents proof of principle modelling techniques, using large deformation, large strain, finite element software. These modelling techniques can be extended to more extensive parameter studies evaluating the effects of pilger process parameter changes on final tube stress and strain states and their relationship to defect formation/propagation. Sensitivity studies on input variables and the process parameters associated with one pass of the pilger process are also included. The modelling techniques have been extended to parameter studies evaluating the effects of pilger process parameter changes on tube stress and strain states and their relationship to defect formation. Eventually a complex qualified 3-D model will provide more accurate results for process evaluation purposes. However, the trends and results reported are judged adequate for examining process trends and parameter variability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".