Multi-skeleton model for top-down design of complex products
Bibliographic record
Abstract
Generally, there are two alternative design approaches available to engineers: bottom-up and top-down. Considering the sharp increase in the complexity of most mechanical products, the top-down design approach is more widely adopted in the development of complex products. However, in traditional top-down design process, design parameters are communicated through single-skeleton models, and design units are strongly coupled due to the multi-dimensional complexity of products. Toward this end, a new top-down design approach based on multi-skeleton model is proposed in this article. First, in accordance with different kinds of design parameters, three major skeleton models are defined, including location skeleton model, published skeleton model, and design skeleton model. And the characteristics of multi-skeleton models are also described. Then, the top-down design process based on the multi-skeleton model is explored, especially in the multi-skeleton modeling phase. It is also illustrated in detail that how to realize design parameter transmission and design unit reuse. Subsequently, it elaborates the communicating way and structure optimization of design parameters to support parameters controlled publishing and design units reuse. Finally, a meteorological satellite and a crawler crane design cases are implemented to expound the feasibility and effectiveness of the proposed framework.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".