Multi-skeleton model for top-down design of complex products
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it