Die stress analysis and improvement of the welding valve fastener in multi-stage forging
Bibliographic record
Abstract
This study explores the multi-stage cold forming die of a welding valve fastener using simulation software. It is possible to understand the various stress intensities of the die core bore and the corresponding distributions during each forging stage so as to improve the service life of the die. These stresses include radial stress, axial stress, hoop stress, and maximum principal stress, as well as the different types of stresses that could cause different fractures of the die core. Therefore, it is necessary to use different die design methods to improve the fracture issues for different die cores. For example, shrink fit can be used between the die core and die case. By adjusting the size of the shrink fit, tensile hoop stress can be converted into compressive hoop stress, which can avoid the generation of axial cracking of the die during the forging formation. In addition, drastic changes in axial stress caused by the stress concentration on the die core can yield a transverse crack of the die core. Thus adopting preventative measures by split such a stress concentration into two sections reduces the drastic changes in axial stress on that section.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".