Analysis of heterogeneous and synthetic data driven artificial intelligence for exploring smart electromagnetic forming (AI4EMF)

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Electromagnetic forming (EMF) has high potential for shaping complex parts and implementing lightweight design concepts, but despite of advantages such as high flexibility and extended forming limits at room temperature for many materials EMF has not made its major breakthrough yet. One reason is that due to complex interactions between thermal, electrical and mechanical field variables, it has hardly been possible to design a manufacturing process including the necessary durable tool coils in a simple and efficient way. Furthermore, due to the very high forming speed hardly any reliable conclusions about the process conditions can be made. Current developments in computing power and new technologies such as live process data analysis, cloud computing, connectivity and controlled production, enable overcoming these challenges. The planned project combines developments in the field of artificial intelligence (AI), numerical modelling and process technology to enable the investigation of a smart EMF process. Data-based AI methods shall ensure systematic process design and control. For this purpose, synthetic data from simulation and domain knowledge are consulted together with conventional process data in a technologically barely explored approach. Additionally, a method to design durable tools for EMF based on comprehensive numerical modelling is developed. Finally, all results are combined and tested based on a demonstrator process.