Application of Machine Learning for Anomaly Detection in Printed Circuit Boards Imbalance Date Set
Bibliographic record
Abstract
The detection of anomalies in printed circuit boards (PCBs) is an important challenge in the electronics manufacturing industry. Traditional anomaly detection methods often struggle to handle imbalanced datasets, which are common in real-world PCB production. In recent years, machine learning (ML) algorithms have emerged as a promising solution to this problem. This study investigates the use of ML algorithms for anomaly detection in PCBs, with a particular focus on addressing the issue of imbalanced data. We propose a data-level technique to balance the dataset and improve the performance of the ML algorithm. Our results show that our approach outperforms traditional methods in terms of precision, recall, and F1 score. Overall, this study demonstrates the potential of ML in addressing the challenge of anomaly detection in PCBs and highlights the importance of considering imbalanced data in such applications.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".