Anomaly Detection in Hot Forming Processes using Hybrid Modeling - Part II

Hot forming is a widely used manufacturing process of crash-relevant structural components with complex geometries. In this paper a previously presented method of anomaly detection is further optimized allowing more data sources to be used in the outlier evaluation process. The method is based on a hybrid model consisting of a physical first-principles model of the hot forming press and a neural network in series. It allows a wide range of sensor data to be considered while keeping the anomaly detection process physically explainable.