Neural network-based estimation and compensation of friction for enhanced deep drawing process control

Neural network-based estimation and compensation of friction for enhanced deep drawing process control

THIERY Sebastian, EL ABDINE Mazhar Zein, HEGER Jens, BEN KHALIFA Noomane

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Abstract. Fluctuating process conditions, such as lubrication, can disturb the production process and lead to faulty components that have cracks or wrinkles. Real-time identification of process parameters can detect deviations in sheet forming operations and enable the process parameters to be adjusted. To increase process robustness, closed-loop control is often used to monitor and influence the material draw-in, which corresponds to the material flow and can be measured by camera systems inside the deep-drawing press. The aim of this work is to develop a control concept that can predict the optimum blank holder force by estimating the coefficient of friction based on the material draw-in of the last stroke. Using a cross-die geometry, it is shown how the material draw-in can be determined experimentally by means of a camera system and numerically by FE simulations. Finally, artificial neural network-based models are trained through simulations and are subsequently tested on a numerical case study in which the coefficient of friction is changed as a disturbance variable and must be compensated for. The widely applicable control concept has the potential to incorporate additional softsensors, for example to determine material properties, and other target variables, such as the punch force, into the optimization algorithm.

Deep Drawing, Material Draw-In, Predictive Modelling, Friction Estimation, Closed-Loop Control, Process Monitoring and Stabilization, Particle Swarm Optimization

Published online 4/24/2024, 10 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: THIERY Sebastian, EL ABDINE Mazhar Zein, HEGER Jens, BEN KHALIFA Noomane, Neural network-based estimation and compensation of friction for enhanced deep drawing process control, Materials Research Proceedings, Vol. 41, pp 1462-1471, 2024


The article was published as article 162 of the book Material Forming

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

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