Designing Scalable Product Families for Black-Box Functions
Bibliographic record
Abstract
Product family design optimization is a cost-efficient concept for achieving the best tradeoff between commonalization and diversification of products. When design functions are computationally intensive and thus viewed as black-boxes, the product family design becomes more challenging. In this study a two-stage platform configuration and product family design optimization method with generalized commonality is proposed for scale-based families involving black-box functions. The platform configuration is unknown and multiple sub-platforms are allowed. In this study, the main parameters used towards the family design include a non-conventional sensitivity analysis, the detachability property of each variable, and the variation of individual optimal values for each design variable. Metamodeling techniques are employed to provide both the non-conventional sensitivity and correlation intensities information, which leads to significant savings in the number of function calls. Efficiency of this method is tested through designing a scalable family of universal electric motors. Compared to a number of previously developed methods, the proposed method yields a design solution with acceptable performance loss after commonalization, and better value for the aggregated preference objective function while satisfying all the performance constraints.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".