Abstract
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.
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© 2013 TMS (The Minerals, Metals & Materials Society)
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Senn, M., Link, N., Gumbsch, P. (2013). Optimal Process Control Through Feature-Based State Tracking Along Process Chains. In: Li, M., Campbell, C., Thornton, K., Holm, E., Gumbsch, P. (eds) Proceedings of the 2nd World Congress on Integrated Computational Materials Engineering (ICME). Springer, Cham. https://doi.org/10.1007/978-3-319-48194-4_11
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DOI: https://doi.org/10.1007/978-3-319-48194-4_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48585-0
Online ISBN: 978-3-319-48194-4
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