Consistency between geometric and dynamic views of a mechanical system
Bibliographic record
Abstract
In this paper, we investigate the problem of automated consistency management between different views of a single system design. As individual view models evolve, consistency is often lost. Ensuring consistency between different views requires periodic concerted efforts from the model designers involved. In general, the detection of inconsistencies and recovering from them is a tedious, error-prone and at best semi-automated process. Automated techniques can alleviate the problem. We focus on a representative sub-set of the problem: consistency between geometric (Computer-Aided Design -- CAD) models of a mechanical system, and the corresponding dynamics simulation models. We illustrate our approach by means of a simple example and use the SolidEdge CAD tool and the Modelica modelling language to model the different views. We analyze how to model a relation between the two views, which will assure consistency. If such a relation can be defined between the two views, consistency verification as well as change propagation to preserve consistency after one of the views has changed must still be derived. We conclude that consistency relation models and the derived change propagation operations can very elegantly be represented using Triple Graph Grammars.
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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 | 0.005 | 0.020 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".