A Framework for Anomaly Classification and Segmentation in Remanufacturing using Autoencoders and Simulated Data

In remanufacturing, reusable end-of-life products must be separated from those for which remanufacturing is not economical. In this process, the focus is initially on the detection and evaluation of surface defects of the used product. Due to the use phase of the product, these show a great variety with strongly differing degrees of severity. Deep-learning-based autoencoders offer the advantage of defect-independent detection of anomalies by detecting deviations from a defect-free product condition. In the present work, anomaly detection based on synthetically generated image data is investigated. In addition, a framework for classification and segmentation is developed and presented. The results show that the approach presented in the paper is effective in detecting anomalies with potential for improvement through adjustments such as perceptual loss or image processing methods. However, challenges remain in accurately detecting anomalies in the presence of missing parts, large anomalies, and image data with high variance.