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 - http://www.scopus.com/inward/record.url?scp=85195938681&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/eddd81b4-a7f7-323f-a16a-77ce83bbfbe3/

U2 - 10.21741/9781644903131-162

DO - 10.21741/9781644903131-162

M3 - Article in conference proceedings

SN - 9781644903131

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 - ESAFORM 2024

Y2 - 24 April 2024 through 26 April 2024

ER -