Adaptable design of open architecture products with robust performance
Bibliographic record
Abstract
Adaptable design is an approach to design adaptable products whose modules/configurations and parameter values can be changed during an operation stage to satisfy different customer requirements. An open architecture product is an adaptable product with open interfaces to allow the third-party vendors to develop new add-on modules and connect these add-on modules through the open interfaces. In this work, an open architecture adaptable product is modelled by a platform, alternative add-on modules, and open interfaces to connect the add-on modules with the platform. Both the specific add-on modules that need to be designed at the product development stage and the unknown add-on modules that could be added in the future are considered. In this research, a novel robust design approach is introduced to identify the optimal design of an open architecture adaptable product whose functional performance measures are the least sensitive to variations of the product and operating parameters due to uncertainties. First, characteristics of open architecture adaptable products are discussed. Methods for modelling of platform, add-on modules, and open interfaces are then introduced. A multi-level optimisation method is subsequently explained to identify the optimal design configuration and parameters considering product performance measures and their variations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".