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Optimal process control through feature-based state tracking along process chains

: Senn, M.; Link, N.; Gumbsch, P.


Li, M.; Campbell, C.; Thornton, K.; Holm, E.A.; Gumbsch, P. ; The Minerals, Metals and Materials Society -TMS-, Warrendale/Pa.:
2nd World Congress on Integrated Computational Materials Engineering, ICME 2013. Proceedings : TMS ICME - manufacturing, design, materials, held July 7 - 11, 2013 at Salt Lake Marriott Downtown at City Creek, Salt Lake City, Utah
Hoboken, NJ: Wiley, 2013
ISBN: 1-118-76689-X
ISBN: 978-1-118-76689-7
World Congress on Integrated Computational Materials Engineering (ICME) <2, 2013, Salt Lake City/Utah>
Conference Paper
Fraunhofer IWM ()
state tracking; optimal control; manufacturing process; process chain

The 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.