Data-driven process modeling in stamping technology

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In the context of digitalization and miniaturization, the demand for complex electronic components is increasing. These are often produced in stamping and bending processes, which are characterized by many forming operations and associated interactions. The complex interactions cannot be fully represented by simulations, so implicit knowledge remains essential. Therefore, the development and use of novel models is necessary to be able to generate additional process understanding. Especially data-driven modeling offers promising potentials in this respect to extract additional knowledge from existing data and thus to formalize implicit knowledge. Therefore, in this project a stamping-bending process will be modeled based on different numerical and real data. Of particular interest here are the defined quality criteria, which can be of both geometric and physical nature. Through the targeted introduction and measurement of disturbance variables, a comprehensive view of the process is made possible. For this purpose, modules are developed that allow to influence the process. A special feature of the proposed modeling approach is the use of a set-based reachability analysis. That is, the problem is formulated such that reachable states are guaranteed to be within a certain set. For forming processes, this means guarantees, e.g., regarding the compliance with certain tolerances. At the same time, the size of the set and distribution of achievable states allows an evaluation of the process stability. Another advantage in contrast to stochastic methods is that clear cause-effect chains can be identified. Thus, this approach promises new possibilities to increase the process understanding. However, this requires the development of new algorithms that allow the problem to be solved. After the model has been built, checked for conformance and formally verified, the validation of the model can take place. For this purpose, components are manufactured again on the basis of defined process parameters and compared with the predictions of the model. This is followed by process optimization based on the model. Subsequent production using the optimized process finally allows a comparison between the processes and a comprehensive evaluation of the predictive capability of the developed model.