Evaluation of General Product Adaptability for Adaptable Product Design
Bibliographic record
Abstract
Abstract Adaptable products are designed for easy change of their configurations and parameters during the operation stage to satisfy the new functional requirements. In the past, significant progress has been achieved in our research on modeling, evaluation, and optimization for adaptable product design. Prediction of specific product adaptation requirements, however, is a challenging task for the design of adaptable products. The objective of this research is to extend our previous work to the evaluation of general product adaptability when specific product adaptation requirements are not given at the design stage, based on our new adaptable product modeling scheme. In this work, various influencing factors in the new modeling scheme, including adaptable and unadaptable components/sub-assemblies, components/sub-assemblies in modules, and uncertainties of components/sub-assemblies in product adaptation, are considered to improve the existing methods for evaluation of the general product adaptability. The developed new evaluation measure has been employed in the development of a new adaptable product design approach, considering cases without or with specific requirements for product adaptations. Case studies have also been implemented for the design of an adaptable device, considering cases without or with specific product adaptation requirements.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".