Auto-Correlated Multivariate Quality Control for Electronic Products Manufacturing with Decomposition Analysis
Bibliographic record
Abstract
Many modern industrial processes involve multiple quality measures, and using individual control charts for each measure can be misleading if these measures are highly related.This paper proposes a new method for statistically controlling electronic products with multiple, interconnected quality characteristics.The method utilizes a combined model: a multivariate autoregressive (MAR) model with neural networks, to handle the presence of both correlation and autocorrelation in the data.The study compares the effectiveness of MCUSUM, MEWMA, and T 2 Hotelling charts in detecting small shifts in the overall process quality.To pinpoint the specific variables causing out-of-control signals in the T 2 Hotelling chart, we introduce a novel decomposition technique.This technique allows us to identify which measures are contributing most to these signals.Additionally, the MCUSUM and MEWMA charts demonstrate excellent performance in detecting small quality changes, leading to faster corrective actions.Overall, these findings suggest that our proposed method can significantly improve the reliability and responsiveness of quality control in electronics manufacturing.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".