This paper focuses on the problem of predicting production line status for Printed Circuit Boards (PCBs). The problem contains three prediction tasks regarding PCB producing process. Firstly, data exploration is carried out and it reveals several data challenges, including data imbalance, data noise, small sample size, and component difference. To predict production line status for components of PCBs using records of inspection on pins, we proposed two possible feature extraction methods to compress the pin-level data into component-level. A statistical feature extraction method, which retrieves descriptive statistics such as mean, standard deviation, maximum, and minimum of pins on the same component, is applied to Task 1, while a PinNumber-based feature extraction method, which keep original values for 2-pin components, is applied to Task3. In addition, a neural-net model with feeding imbalance control is established for Task 1. and a random forests model is applied for both Task 2 and Task 3. Moreover, a threshold moving technique is proposed to optimize the threshold selection. Finally, the result shows that our models achieved f1-scores of 0.44, 0.54, and 0.71 on the test set for the three tasks, respectively.
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Prediction, Printed Circuit Boards
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