Online Quality Prediction in Windshield Manufacturing using Data-Efficient Machine Learning

The digitization of manufacturing processes opens up the possibility of using machine learning methods on process data to predict future product quality. Based on the model predictions, quality improvement actions can be taken at an early stage. However, significant challenges must be overcome to successfully implement the predictions. Production lines are subject to hardware and memory limitations and are characterized by constant changes in quality influencing factors. In this paper, we address these challenges and present an online prediction approach for real-world manufacturing processes. On the one hand, it includes methods for feature extraction and selection from multimodal process and sensor data. On the other hand, a continual learning method based on memory-aware synapses is developed to efficiently train an artificial neural network over process changes. We deploy and evaluate the approach in a windshield production process. Our experimental evaluation shows that the model can accurately predict windshield quality and achieve significant process improvement. By comparing with other learning strategies such as transfer learning, we also show that the continual learning method both prevents catastrophic forgetting of the model and maintains its data efficiency.