Poster Abstract: Anomaly Detection in Surface Mount Technology Process using Multi-modal Data

Anomaly detection is an important area for both research and real-world applications. In the surface mounting technology (SMT) process, the defectives of solder paste printing need to be detected immediately or it may cause great effort for recycling and slow down the whole process. In this paper, we propose a novel model, MM-DNN, for anomaly detection with multi-modal data. We collect a multi-modal dataset from different sensors in the factory. Our method efficiently extracts both predictive features for classification and correlative features between multi-modal data to achieve a higher detection rate. As shown in the experiment, our method can further reduce 77% false alarm rate of the detection result in the factory while keeping 95% of real defectives be correctly detected.