Anomaly Detection in the Automotive Stamping Process: An Unsupervised Machine Learning Approach

Abstract In metal forming, such as stamping of automotive parts, unsupervised machine learning models offer a transformative approach to real-time quality control, especially when labelled data are scarce. Leveraging clustering algorithms and autoencoders, we develop a machine learning system capable of autonomously monitoring sensor data and identifying deviations suggestive of potential defects. The system offers multiple benefits including rapid intervention, reduced part defects and lower stoppages required to rectify defects. The use of unsupervised machine learning models also adds a layer of adaptability, allowing the system to continually refine its understanding of what constitutes a ‘normal’ operation. Empirical evaluation demonstrates the potential of the developed system in detecting anomalies in production data collected from dynamic automotive manufacturing environments.