Towards Rapid Redesign: Pattern-Based Design Diagnostics for Large-Scale and Complex Redesign Problems
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
This paper presents our continued research efforts towards developing a decomposition-based solution approach for rapid computational redesign to support agile manufacturing of evolutionary products. By analogy to the practices used for physical machines, the proposed approach involves two general steps: diagnosis and repair. This paper focuses on the diagnosis step. for which a two-phase decomposition method is developed. The first phase, called design dependency analysis, systematizes and reorganizes the intrinsic coupling structure of the existing design model by analyzing and reordering the design dependency matrix (DDM) used to represent the functional dependence and couplings inherent in the design model. The second phase, called redesign partitioning analysis, uses this result to generate alternative redesign pattern solutions through a three-stage procedure. Each pattern solution delimits the portions of the design model that need to be re-computed. An example problem concerning the redesign of an automobile powertrain is used for method illustration. Our seed paper has presented a method for selecting the optimal redesign pattern solution from the alternatives generated through redesign partitioning analysis, and a sequel paper will discuss how to generate a corresponding re-computation strategy and redesign plan (redesign shortcut roadmap).
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 | 0.001 | 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) | 0.001 | 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