Robust Design for Product Adaptation Considering Changes in Configurations and Parameters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This paper introduces a robust design method for product adaptation considering uncertainties in both product configurations and parameters. In this study, probability of product adaptation in the operation stage and influence of the probability on the optimal design solution are investigated. In this work, an AND-OR tree is used to model feasible design candidates and their adaptations, where each node represents a partial solution for the original design or the adapted design. Design candidates are generated from the AND-OR tree through tree-based search, and a design candidate can be defined by variation nodes that are used for potential product adaptations. A multi-level optimization method is applied to obtain the optimal values of design parameters for each design candidate and the best design solution from all feasible candidates. Both evaluation measures and their variations are considered in this robust design method.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it