Design strategy for optimal iterative learning control applied on a deep drawing process: Recognising that stamping and deep-drawing operations are repetitive processes—which can learn and improve based on process history

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Abstrakt

Metal forming processes in general can be characterised as repetitive processes; this work will take advantage of this characteristic by developing an algorithm or control system which transfers process information from part to part, reducing the impact of repetitive uncertainties, e.g. a gradual changes in the material properties. The process is highly non-linear and the system plant is modelled using a non-linear finite element and the gain factors for the iterative learning controller is identified solving a non-linear optimal control problem. The optimal control problem is formulated as a non-linear least square problem where the system response is evaluated using a non-linear finite element model of the process.
OriginalsprogEngelsk
TidsskriftInternational Journal of Advanced Manufacturing Technology
Vol/bind88
Udgave nummer1-4
Sider (fra-til)3–18
Antal sider16
ISSN0268-3768
DOI
StatusUdgivet - jan. 2017

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