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

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Authors

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.
Original languageEnglish
Title of host publicationMaterial Forming ESAFORM 2024 : The 27th International ESAFORM Conference on Material Forming – ESAFORM 2024 – held in Toulouse (France), at the Pierre Baudis Convention Center between 24-26th April, 2024
EditorsAnna Carla Araujo, Arthur Cantarel, France Chabert, Adrian Korycki, Philippe Olivier, Fabrice Schmidt
Number of pages10
Place of PublicationMillersville
PublisherMaterialsResearchForum LLC
Publication date15.05.2024
Pages1462-1471
Article number162
ISBN (print)9781644903131
ISBN (electronic)978-1-64490-313-1
DOIs
Publication statusPublished - 15.05.2024
Event27th International ESAFORM Conference on Material Forming - ESAFORM 2024 - Pierre Baudis Convention Center, Toulouse, France
Duration: 24.04.202426.04.2024
Conference number: 27
https://esaform24.fr/

Bibliographical note

Publisher Copyright:
© 2024, Association of American Publishers. All rights reserved.

    Research areas

  • Engineering - Deep Drawing, Material Draw-In, predictive modelling, Friction Estimation, closed-loop control, Process Monitoring and Stabilization, Particle Swarm Optimization

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