Data-based die spotting in sheet metal forming

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30 % of the development costs of a tool in sheet metal forming are due to the tool machining. The causes are manufacturing inaccuracies, elastic deformations of all elements in the force flow and the change in sheet thickness during forming. Since it is not realistic to consider all influences and uncertainties in simulation models, their effects can only be corrected on the real die. The run-in includes the spotting and mechanical machining of the active surfaces to produce a good part based on a uniform spotting pattern and a defined material flow. The die spotting is manual, time-consuming and experience-based work with strong physical stress. Heterogeneous data and abstract information must be processed and interactions with subsequent processes must be taken into account. The shortage of skilled workers is forcing the scientific community to create foundations for automating tool familiarization. No method of correlation between spotting image and the amount of ablation has been found. No mathematical formalization of an incorporation strategy has been described. No automated solutions for die spotting could be identified. Finally, no transfer of the trained tool state to subsequent generations of tools has been documented. Promising methods for automatable die spotting are seen in the combination of different AI approaches. The following research questions (FF) need to be answered and hypotheses (H) tested: FF 1: Which type of NN with which mesh topology is suitable for automated generation of the active surfaces of the forming tools considering the tool-machine interaction? H 1.1: NNs with the ability to process spatial data can solve the above design problem. H 1.2: Representation learning or symbolic AI algorithms represent other solutions. H 1.3: Pre-training with simulations increases the robustness of ML models despite small amounts of data. FF 2: What type and topology of NN is suitable for learning the design function in terms of machine parameters such as force and velocity histories? H 2.1: The design function identifies parameters based on descriptions of the forming problem and the machine. A formal description must be defined for both. Again, pre-training NNs on simulation data could provide a solution. FF 3: How can the task of die spotting be automated? H 3.1: A camera-based system captures and analyzes spotting images of the active surfaces and 2D images of the formed part to determine the amount of ablation required. H 3.2: Optimized machine and process parameters can be calculated based on previous data and simulations. H 3.3: Experiences from learning function g (tool familiarization) can be used to improve function f (tool design).