A modular design approach for modeling and optimization of adaptable products considering the whole product utilization spans
Bibliographic record
Abstract
Design of adaptable products aims at satisfying different and changing customer requirements through changes of products such as reconfiguration and upgrading during their utilization stages. In this research, a new modular design approach is introduced for modeling and optimization of adaptable products considering the whole product utilization spans. In this work, product descriptions in different operation phases are modeled by different configurations, and each of these configurations is described by parameters. The product components with similar life-cycle properties such as operation phases and life-spans are grouped into modules based on a fuzzy pattern clustering method. A hybrid AND–OR tree is used to model all feasible design solutions considering different configurations and their parameters. The adaptable product is evaluated by different evaluation measures with different units, which are further converted into comparable evaluation indices. The overall evaluation index for an adaptable product is defined by individual evaluation indices and their importance weighting factors considering the whole product utilization span. A multilevel optimization method is employed to identify the best design solution, its configurations in different operation phases and parameter values of the relevant configurations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".