Active shovel spinning process: Plastic deformation behavior, microstructure, and properties
Bibliographic record
Abstract
In response to the challenges of traditional casting and welding processes for thin disc with cylinder, characterized by excessive weight, complex procedures, and variable seam quality, a novel active shovel spinning (ASS) process is proposed. This technique utilizes a spinning roller to lift the sheet metal radially, gradually forming the cylindrical wall into a monolithic structure. A finite element (FE) analysis model for the ASS of SPHE material is established, with the optimization goals set as the outer diameter ellipticity O ¯ r , the precision of the outer generatrix L ¯ I , and the maximum forming force F max of the spinning roller. Using extreme difference analysis and the Grey Relational Analysis (GRA), multi-objective optimization under various conditions of rotational speed ratio η , feed rate v , and friction coefficient f is conducted to identify the optimal combination of process parameters as η =1.6, v = 2, and f = 0.15. The plastic flow behavior under these parameters is analyzed, and experimental studies are conducted. The findings reveal extensive metal flow in radial, axial, and circumferential directions. The bulging of the cylinder primarily relies on the deformation of the metal at the upper ends of the inner and outer sides, with greater stress and strain on the outer wall than the inner. The disparity between the deformed and undeformed areas is mainly reflected in the position and diameter of the stress Mohr's circle and the size of the strain Mohr's circle diameter. Due to the increased aspect ratio, refinement, and uniformity of the grains, the ultimate tensile strength σ UTSi in all directions within the forming area has significantly improved, with a 48 % increase in the reduced thickness area σ UTS1 CD and a 31 % increase in the σ UTS2 CD . These results enhance the understanding of the forming mechanism in the ASS process, thereby providing crucial guidance for optimizing the quality of the formed components.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".