Control Charts for Short Production Runs in Aerospace Manufacturing
Bibliographic record
Abstract
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 Q-chart, one of the available self-starting control charts, in comparison with I/MR-X chart for short production runs. The preliminary results suggest that a combination of Cusum Q-chart and Cusum I/MR-X chart be considered for better quality monitoring and control.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".