Semi-Supervised Domain Adaptation for Pick Classification in Pick and Place Machines

Abstract: Pick and Place (PnP) machines collect and use large amounts of image data of Printed Circuit Board (PCB) components. The data is used to train automated image analysis methods to improve the decisions in the mounting process. Previous work with Neural Networks has shown promising results in the classification of the component status. However, the characteristics of the data changes over time as new PCBs, components, and PnP machines are deployed. This work applies a Semi-supervised Domain Adaptation method named Associative Domain Adaptation to enable learning of a new and unlabeled data set. The networks reach high performance despite skew class distributions, but the final results do not outperform the current classification algorithm in the PnP machines. However, ensembling of different methods can make use of the strengths from both the current classification system and the method proposed in this thesis, where the ability to learn from unlabeled data is a promising advantage.