Robust Design for Product Adaptation Considering Changes in Configurations and Parameters
Bibliographic record
Abstract
Abstract Adaptable products are designed such that their configurations and parameters can be changed easily in the operation stage to satisfy changes in functional requirements. Design of adaptable products can extend lifespans of these products. A new robust adaptable product design method is introduced in this research to identify the optimal design including the product configurations and parameter values considering uncertainties in both product configurations and parameters. In this work, an AND-OR tree is used to model feasible design candidates and their configurations considering product adaptations, where each node represents a partial design solution. Different design candidates are created from the AND-OR tree through tree-based search, and a design candidate is defined by configurations of the original design and the adapted designs. Each configuration is further defined by parameters. A multi-level optimization method is used to obtain the optimal adaptable product design including its configurations and parameter values of these configurations. In this study, uncertainties of configurations are defined by probabilities for production adaptations, while uncertainties of parameters are defined by variations of parameter values. Both evaluation measures and their variations are considered in this robust adaptable product design method. A case study has been implemented to show how the developed method is used for the design of an adaptable mechanical system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".