Parametric Design of Batch Flender’s Gear Units Based on Pro/Engineer
Bibliographic record
Abstract
On the basis of studying the structure and design specification of Flender’s gear units, a method of parametric design and modeling for batch gear units based on Pro/Engineer has been adopted. According to given dimensions, we has initially defined variables in Pro/Engineer and established a family table which comprises all the required dimensions, then assigned variables or equations to corresponding feature dimensions during modeling. The model of a gear unit consists of two sections: one is the fundamental shape, the dimensions of which are all given; the other is the external embellish, including the bosses around the axes, the oil gallery for lubrication, the inspecting hole and so on, the dimensions of which are to be designed and determined by designers so as to create an ideal figure of the case. Thus, each series of gear units merely requires a single model, so that all parts in the entire series of gear units shall be generated through the family table and saved as an individual product respectively. The experimental results demonstrate that the development period is shortened, the cost is reduced and the productivity is increased.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".