A Feature Engineering-based Method for PCB Solder Paste Position Offset Prediction

Solder paste printing position offset is a common type of defective printed circuit boards (PCBs) printing, and accurate position offset prediction helps to avoid the production of defects, thus improving efficiency. The existing methods mainly use the powerful nonlinear fitting ability of deep learning to learn the variation pattern of solder paste printing quality to achieve a good prediction. However, factories also focus on the interpretability of the model, and existing methods are difficult to give the basis for decisions, so there are still limitations in the practical application. To solve this problem, we propose a Support vector machine (SVM) approach, in which we manually design 14 statistical features based on the original data, then the resampling reduces the effect of data imbalance and achieves PCB pad-level offset prediction. Finally, we verified about six-day of real solder paste printing production data and achieved good experimental results.