Neural network-based estimation and compensation of friction for enhanced deep drawing process control
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Neural network-based estimation and compensation of friction for enhanced deep drawing process control. / Thiery, Sebastian; Zein El Abdine, Mazhar; Heger, Jens et al.
Material 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. ed. / Anna Carla Araujo; Arthur Cantarel; France Chabert; Adrian Korycki; Philippe Olivier; Fabrice Schmidt. Millersville : MaterialsResearchForum LLC, 2024. p. 1462-1471 162 (Materials Research Proceedings; Vol. 41).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - Neural network-based estimation and compensation of friction for enhanced deep drawing process control
AU - Thiery, Sebastian
AU - Zein El Abdine, Mazhar
AU - Heger, Jens
AU - Ben Khalifa, Noomane
N1 - Conference code: 27
PY - 2024/5/15
Y1 - 2024/5/15
N2 - 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.
AB - 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.
KW - Engineering
KW - Deep Drawing
KW - Material Draw-In
KW - predictive modelling
KW - Friction Estimation
KW - closed-loop control
KW - Process Monitoring and Stabilization
KW - Particle Swarm Optimization
UR - https://d21zja6o12zyp0.cloudfront.net/9781644903131.pdf
U2 - 10.21741/9781644903131-162
DO - 10.21741/9781644903131-162
M3 - Article in conference proceedings
T3 - Materials Research Proceedings
SP - 1462
EP - 1471
BT - Material Forming ESAFORM 2024
A2 - Araujo, Anna Carla
A2 - Cantarel, Arthur
A2 - Chabert, France
A2 - Korycki, Adrian
A2 - Olivier, Philippe
A2 - Schmidt, Fabrice
PB - MaterialsResearchForum LLC
CY - Millersville
T2 - 27th International ESAFORM Conference on Material Forming
Y2 - 24 April 2024 through 26 April 2024
ER -