Senn, M.M.SennLink, N.N.LinkGumbsch, P.P.Gumbsch2022-03-122022-03-122013https://publica.fraunhofer.de/handle/publica/38165910.1002/9781118767061.ch11The optimal control of a manufacturing process aims at control parameters that achieve the optimal result with least effort while accepting and handling uncertainty in the state space. This requires a description of the process which includes a representation of the state of the processed material. Only few observable quantities can usually be measured from which the state has to be reconstructed by real-time capable and robust state tracker models. This state tracking is performed by a mapping of the measured quantities on the state variables which is found by nonlinear regression. The mapping also includes a dimension reduction to lower the complexity of the multi-stage optimization problem which is approximately solved online. The proposed generic process model provides a universal description that can be adapted to specific data from simulations or experiments. We show the feasibility of the generic approach by the application to two deep drawing simulation models.enstate trackingoptimal controlmanufacturing processprocess chainconference paper