TOOLING DESIGN IMPROVEMENT OF MULTISTAGE COLD FORGING OF SPECIALTY SHAPED NUTS USING CAE AND 3D PRINTING
Bibliographic record
Abstract
Given its critical role in the fastener industry, cold forging is widely performed in automotive production, manufacturing, aviation, and 3C products. Personnel experience and trying error approaches provide a subjective and unreliable background despite their extensive use in fastener forming and die design owing to the difficulty in controlling the development schedule. This study used DEFORM-3D analysis software to investigate the die service life from producing specialty shaped nuts in a multistage cold forging process. Effective stress, effective strain, velocity field, and other quantitative metrics of dies and work-pieces can be obtained from numerical simulations. Herein, 3D printing technology is also implemented to create cold forging dies and deformed work-pieces for assessing the dimension of tooling assembly. This process allows engineers to gain a better understanding of the tooling design at development phase and derives the parts, which were previously just simulation results from DEFORM-3D forming software. Results can help a multistage processing factory establish a cold forming capacity for the development of new products. Consequently, the ability of self-design and self-manufacture of specialty shaped fasteners in Taiwan could be increased widely to enhance the international competitiveness of domestic industries.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".