3D printing has established itself as a production process and has also found its way into the fashion industry. Individualised shoes can be 3D printed, but this poses significant challenges for automated quality control, as defects are rare. Autoencoders enable to train a system with defect-free data so that detected deviations from this state can be evaluated as defects. Our research shows a ROC AUC score of 0.87, proving that this method is suitable for anomaly detection in 3D-printed shoes.
