Multi-Analogy Innovation Design Based on Digital Twin
Bibliographic record
Abstract
Analogy-based design is an effective approach for innovative design. However, existing research on analogy design mainly focuses on methods to form innovative schemes, without considering feasibility or practicality in applications. This research proposes a multi-analogy innovation design (M-AID) model based on analogy in both design-centric complexity (DCC) and solution of inventive problems (TRIZ). To improve practicality, digital twin (DT) is introduced to apply real design information, manufacturing production data, and maintenance information in the design process. The method includes six steps: (1) analyze a target product based on users and market requirements to synthesize general function requirements; (2) acquire analogy function source using knowledge base and patent base; (3) call digital twin resources to obtain real product data for the design; (4) reduce the complexity of the design system after fusion using DCC theory; (5) use TRIZ to solve problems of design conflicts; and (6) evaluate design solutions according to product requirements. The current proposed method enhances the design scheme feasibility and reduces the number of iterations from the conceptual scheme to the final scheme in the design process, thus improving the efficiency of the innovative design process. The applicability of the currently proposed method is demonstrated through exemplification of innovative design of a dust removal system for a solar panel.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".