Optimal Mean and Tolerance Allocation Using Conformance‐based Design
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In this paper, we invoke probability constrained optimization to establish a framework for allocating means and tolerances in design for quality that focuses on customer satisfaction at predictable cost levels. The optimal allocation minimizes the production costs while ensuring that responses conform probabilistically to their specification limits. An overall system probability of conformance is obtained from a quality policy (e.g. defect rate, process capability index). Probabilities are evaluated using limit‐state functions and fast integration methods. The three quality metrics (i.e. target/larger/smaller‐is‐best) and robustness are addressed naturally. The methodology is developed in detail and compared with the traditional minimum total cost approach. Optimal means and tolerances are found for an electro‐mechanical servo system and a power division circuit to illustrate the practicality and potential of the approach. Copyright © 2005 John Wiley & Sons, Ltd.
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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 | 0.005 | 0.001 |
| 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