Part-to-Part model predictive control - Using a modified Gauss-Newton scheme

B. Endelt*


Publikation: Bidrag til tidsskriftKonferenceartikel i tidsskriftForskningpeer review

1 Citationer (Scopus)


Over the last two decades there has been and increasing academic and industrial focus on process robustness and process control applied on sheet-metal forming processes and a high number of control schemes has been proposed. However, many of proposed control schemes rely on process data sampled during the punch stroke (in case of stamping and deep drawing) e.g. draw-ill sensors and the absent of robust sampling technologies is on of the main barriers preventing industrial implementation. One approach to avoid in-process sampling is to apply an part-to-part control strategy, where the process parameters are updated based on post-processing samples e.g. flange geometry. Different algorithms has been proposed, the current article will adapt the algorithm proposed by Endelt (2017) which are based on a Gauss-Newton formulation (non-linear least-square). The control scheme is applied on the NumiSlieet'08 S-rail benchmark, the algorithm has previously been applied on a square deep-drawing with good results and the present paper illustrate the flexibility of the proposed part-to-part control scheme, as the algorithm can be directly adopted to s-rail process using the design rule from the original paper.

TidsskriftIOP Conference Series: Materials Science and Engineering
Udgave nummer1
StatusUdgivet - 26 nov. 2019
Begivenhed38th International Deep Drawing Research Group Annual Conference, IDDRG 2019 - Enschede, Holland
Varighed: 3 jun. 20197 jun. 2019


Konference38th International Deep Drawing Research Group Annual Conference, IDDRG 2019
SponsorAutoForm, TATA Steel, University of Twente

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© Published under licence by IOP Publishing Ltd.


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