Joint determination of optimum process mean, production run length and specification limits for a deteriorating process
Bibliographic record
Abstract
Most of the recent work found in literature solved the problem of determining the optimum values of process parameters by considering one or jointly two parameters using separate models under different assumptions. The objective of this work is to develop a trine model that can be used for joint determination of three process parameters, namely: optimum process mean, production run length and specification limits, under mixed quality loss function for processes that are subject to deterioration over time. This paper will summarise the recent related literature and outlining the technical information required for this work. In this work, the problem will be tracked in two ways: by minimising the total loss and by maximising the net profit. For achieving that, we developed different models that can be used to determine optimum values for process parameters; the analysis leads to the development of the trine model. Numerical examples parallel to each model are presented to illustrate its use in determining the desired optimum parameter value. Sensitivity analysis for different process parameters are also presented to study their effects on the net profit in the view of satisfying the manufacturing requirements.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".