The role of associations in CAD and PLM for handling change propagation during product development
Bibliographic record
Abstract
Current Computer Aided Design (CAD) systems can capture some of the design intent by creating associations between objects. This increases the productivity during product development, and helps maintain the coherence of the product definition when handling engineering changes. CAD systems establish some associations as well as their use at a rather low level of abstraction, e.g. a parallelism constraint. Product Lifecycle Management (PLM) systems, on the other hand, use associations at a higher abstraction level, generally between files. The associations handled by these systems do differ both in terms of abstraction level and formalism of the knowledge they encapsulate. Moreover, limited connections exist between the associations manipulated in both systems, so that handling change propagation in concurrent engineering remains an issue. In this paper, we propose a taxonomy and a model of the different types of associations required to support the set of tasks in the area of product development. The terms on which the taxonomy relies are: association, relation, link, and constraint. The proposed model, named RLC, uses the concepts of aggregation and decomposition to relate Relations, Links, and Constraints.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".