Control Charts for Short Production Runs in Aerospace Manufacturing
Why this work is in the frame
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Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Statistical process control (SPC) has been extensively used in many different industries including automotive, electronics, and aerospace, among others. SPC tools such as control charts, process capability analysis, sampling inspection, etc., have definitive and powerful impact on quality control and improvement for mass production and similar production systems. In aerospace manufacturing, however, applications of SPC tools are more challenging, especially when these tools are implemented in processes producing products of large sizes with slower production rates. For instance, following a widely accepted rule-of-thumb, about 100 units of products are required in the first phase of implementing a Shewhart type control chart. Once established, it then can be used for process control in the second phase for actual production process monitoring and control. In many aerospace production processes, however, it requires that quality control measures be in place from the time that the first unit of product is produced. Certain types of control charts for self-starting (without the first phase) and for short production runs (as few as 3 units) have been developed by researchers and practitioners. They have been tested and used in places where traditional Shewhart control charts are difficult to apply. In this work, we used Monte-Carlo simulation to study the suitability of applying <i>Q</i>-chart, one of the available self-starting control charts, in comparison with I/MR-<i>X</i> chart for short production runs. The preliminary results suggest that a combination of Cusum <i>Q</i>-chart and Cusum I/MR-<i>X</i> chart be considered for better quality monitoring and control.</div></div>
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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 | 0.001 | 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