Using Style Transfer to Leverage Synthetic Data for Machine Learning-Based Quality Inspection in Forming Processes

Camera-based measurement systems are increasingly used in manufacturing, with machine learning models outperforming traditional image recognition methods. However, industrial adoption remains limited, partly due to the effort required for data collection and model training, which typically relies on real manufacturing data. Many mid-sized companies that build the machines do not have the required personnel to set up and train these systems. In addition, the time it takes to implement these networks either delays the start of manufacturing or prevents them from being implemented until after manufacturing has started. A possible solution is training models on synthetic data, such as Computer Aided Design (CAD) renderings, instead of real manufacturing images. However, differences in appearance between renderings and real images lead to poor model performance due to the domain gap, which is the difference between the appearance of the photos and the renderings. This paper proposes an AI-based visual quality inspection method using synthetic training data, bridging the domain gap with a style transfer applied only to the training set. This approach is evaluated on two use cases which are implemented into the industrial PC (IPC) connected to an industrial high-speed press and evaluated and compared to baselines on real manufacturing photos from this process. Results show that the domain gap between synthetic and real images can be closed through style transfer. When using the same product as a style reference, an Intersection over Union (IoU) of over 96% is achieved.