Integrated multiresponse parameter and tolerance design with model parameter uncertainty
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 Integrated parameter and tolerance design is a cost‐effective method to multiresponse quality improvement. However, previous methods usually ignore model parameter uncertainty, dispersion effect, or correlation among responses. This may lead to the obtained optimal solutions far from the true optimal values of parameters and tolerances. To address the problem, a novel integrated parameter and tolerance design method is proposed to solve correlated multiple response problems under consideration of model parameter uncertainty, the location and dispersion effects of the quality loss, and the tolerance costs simultaneously. As there usually exists uncertainty in the quality loss and tolerance costs, multiobjective optimization is adopted to seek for the robust optimal solutions. The effectiveness and robustness of the proposed method are illustrated with a practical example and a random simulation example. The results show that the proposed method provides more reasonable results in quality improvement and cost reduction than those of the existing methods.
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 | 0.005 | 0.005 |
| 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 | 0.000 |
| 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