Model-based Rapid Redesign Using Decomposition Patterns
Bibliographic record
Abstract
This paper presents a pattern-based decomposition methodology for rapid redesign to support design customization in agile manufacturing of evolutionary products. The methodology has three functional phases. The first phase, called design dependency analysis, systematizes and reorganizes the intrinsic coupling structure of a given existing design model that is represented using the design dependency matrix. The second phase, called redesign partitioning analysis, generates alternative redesign pattern solutions to form a solution selection space through a three-stage procedure. The third phase, called pattern selection analysis, finds an optimal redesign pattern solution that entails the least potential redesign effort (in the subsequent solution process). Each pattern solution identifies and delimits the portions of the design model that need to be recomputed, thus expediting the redesign solution process. In such a way, one can treat the recomputation of the entire model, which is a conventional and computation-expensive solution approach, only as the last resort to solve the redesign problem given. An example redesign problem is used for the methodology illustration.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".