Designing Scalable Product Families for Black-Box Functions
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
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.
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.
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 | 0.001 |
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