Identification of the optimal original design configuration, adapted design configuration, and product adaptation process for design of new adaptable product
Bibliographic record
Abstract
Adaptable products can be changed in configurations and parameters during their operation stages to satisfy changes in functional requirements and environmental conditions. The objective of this research is to develop a new method to identify the original design configuration, adapted design configuration, and product adaptation process for the design of a new adaptable product. This research was initiated from the activities to convert the traditional internal combustion engine (ICE) vehicles into electric vehicles (EVs). In this method, the generic adaptable product design considering alternative solutions of original design configurations, adapted design configurations, and product adaptation processes is modelled by an AND-OR tree. Nodes of this tree are defined by unadaptable design nodes, adaptable design nodes, and adaptation process nodes. Design and process nodes are further associated with design and process parameters. Solution of the adaptable design with the optimal original design configuration, adapted design configuration, and product adaptation process is identified through multi-level optimisation (i.e. configuration/process optimisation and parameter optimisation). The 2022 Toyota Camry has been selected as the baseline product for the design of a new adaptable vehicle in this research to demonstrate the effectiveness of the newly developed method.
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.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".