The demand for Printed circuit boards (PCBs) has increased due to the rapid change in technology in recent years. Consequently, PCBs health assessment and fault detection play an important role in improving productivity. This study proposed a novel method which focused on feature engineering for health assessment in PCBs. The performance of the proposed method has been validated using data obtained from PHM Europe 2022 data challenge. In this data challenge, PCBs health assessment needs to be performed with data from the Solder Paste Inspection (SPI) and the Automated Optical Inspection (AOI) machine. The challenge has three tasks: 1) Predict the labels of the AOI machine using the SPI data. 2) Using both the SPI and AOI machine data, predict the operator's verification that the AOI machine correctly detected a defect. 3) With the SPI and AOI data, predict the classification of the defective PCBs as either repairable or unrepairable. The component level features are extracted from the original SPI and AOI data which contain the pin level features to solve these tasks. Two machine learning-based classification models, i.e., Light Gradient Boosting Machine (LightGBM) and eXtreme Gradient Boosting (XGBoost), have been used for classification purposes. Training data given by the organizer was divided into 70% training and 30% validation. Based on the validation data, the highest F1-score was observed with LightGBM in Tasks 1 and 2, whereas, in Task 3, the highest F1-score was observed with the XGBoost model. Hence, the LightGBM model has been used in Tasks 1 and 2, and the XGBoost model was developed for Task 3.
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Health Assessment, Printed Circuit Boards, data challenge,
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