X-ray transmission imaging of waste printed circuit boards for value estimation in recycling using machine learning.

The growing amount of electronic waste is a global challenge: on one hand, it poses a threat to the environment as it may contain toxic or hazardous substances, on the other hand it is a valuable 'urban mine' containing metals like gold and copper. Thus, recycling of electronic waste is not only a measure to reduce environmental pollution but also economically reasonable as prices for raw materials are rising. Within electronic waste, printed circuit boards (PCBs) occupy a prominent position, as they contain most of the valuable material. One important step in the overall recycling process is the evaluation and the value estimation for further treatment of the waste PCBs (WPCBs). In this article, we introduce a method for value estimation of entire WPCBs based on component detection. The value of the WPCB is then predicted by the value of the detected components. This approach allows a flexible application to different situations. In the first step, we created a dataset and labelled the components of 104 WPCBs using different component classes. The component detection is performed on dual energy X-ray images by the deep neural object detection network 'YOLO v5'. The dataset is split into a training, validation and test subset and standard performance measures as precision, recall and F₁-score of the component detection are evaluated. Representative samples from all component classes were selected and analysed for the valuable materials to provide the ground truth of the value estimation in the subsequent step.