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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

Standard

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

Harvard

Thiery, S, Zein El Abdine, M, Heger, J & Ben Khalifa, N 2024, Neural network-based estimation and compensation of friction for enhanced deep drawing process control. in AC Araujo, A Cantarel, F Chabert, A Korycki, P Olivier & F Schmidt (eds), 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., 162, Materials Research Proceedings, vol. 41, MaterialsResearchForum LLC, Millersville, pp. 1462-1471, 27th International ESAFORM Conference on Material Forming - ESAFORM 2024, Toulouse, France, 24.04.24. https://doi.org/10.21741/9781644903131-162

APA

Thiery, S., Zein El Abdine, M., Heger, J., & Ben Khalifa, N. (2024). Neural network-based estimation and compensation of friction for enhanced deep drawing process control. In A. C. Araujo, A. Cantarel, F. Chabert, A. Korycki, P. Olivier, & F. Schmidt (Eds.), 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 (pp. 1462-1471). Article 162 (Materials Research Proceedings; Vol. 41). MaterialsResearchForum LLC. https://doi.org/10.21741/9781644903131-162

Vancouver

Thiery S, Zein El Abdine M, Heger J, Ben Khalifa N. Neural network-based estimation and compensation of friction for enhanced deep drawing process control. In Araujo AC, Cantarel A, Chabert F, Korycki A, Olivier P, Schmidt F, editors, 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. Millersville: MaterialsResearchForum LLC. 2024. p. 1462-1471. 162. (Materials Research Proceedings). doi: 10.21741/9781644903131-162

Bibtex

@inbook{f4e2ddf944b246ee9973d79801d783f5,
title = "Neural network-based estimation and compensation of friction for enhanced deep drawing process control",
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.",
keywords = "Engineering, Deep Drawing, Material Draw-In, predictive modelling, Friction Estimation, closed-loop control, Process Monitoring and Stabilization, Particle Swarm Optimization",
author = "Sebastian Thiery and {Zein El Abdine}, Mazhar and Jens Heger and {Ben Khalifa}, Noomane",
note = "Publisher Copyright: {\textcopyright} 2024, Association of American Publishers. All rights reserved.; 27th International ESAFORM Conference on Material Forming - ESAFORM 2024, ESAFORM 2024 ; Conference date: 24-04-2024 Through 26-04-2024",
year = "2024",
month = may,
day = "15",
doi = "10.21741/9781644903131-162",
language = "English",
isbn = "9781644903131",
series = "Materials Research Proceedings",
publisher = "MaterialsResearchForum LLC",
pages = "1462--1471",
editor = "Araujo, {Anna Carla} and Arthur Cantarel and France Chabert and Adrian Korycki and Philippe Olivier and Fabrice Schmidt",
booktitle = "Material Forming ESAFORM 2024",
address = "United States",
url = "https://esaform24.fr/",

}

RIS

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 -

Other publications by the same author(s)

Analysis of tailored residual stress fields of deep rolled AA2024 sheets

Lehmann, J., Pöltl, D., Esterl, F., Kashaev, N. & Ben Khalifa, N., 09.2025, In: International Journal of Material Forming. 18, 3, 12 p., 66.

Research output: Journal contributions › Journal articles › Research › peer-review

Automatisierte Generierung von Simulationsmodellen für Produktions- und Logistikprozesse mithilfe LLM-basierter Multi-Agenten-Systeme

Krämer, R. & Heger, J., 21.10.2025, Simulation in Produktion und Logistik 2025: 21. ASIM-Fachtagung. Rank, S., Kühn, M. & Schmidt, T. (eds.). Dresden: Dresden University of Technology, 10 p. 57. (ASIM-Mitteilung ; vol. 194).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Comparison of different machine control modes during friction extrusion of AA2024

Rana, H., Chan, C., Suhuddin, U. F. H., Ben Khalifa, N. & Klusemann, B., 2025, 28th International ESAFORM Conference on Material Forming, ESAFORM 2025. Carlone, P., Filice, L. & Umbrello, D. (eds.). Association of American Publishers, p. 772-779 8 p. (Materials Research Proceedings; vol. 54).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Comprehensive analysis of the forming zone and improvement of diameter reduction prediction in the dieless wire drawing process

Braatz, M., Bohlen, J. & Ben Khalifa, N., 15.04.2025, In: Journal of Manufacturing Processes. 139, p. 210-223 14 p.

Research output: Journal contributions › Journal articles › Research › peer-review

Connecting texture development to die design in extruded flat products

Esterl, F., Nienaber, M., Bohlen, J. & Ben Khalifa, N., 31.01.2025, In: Journal of Manufacturing Processes. 134, p. 891-903 13 p.

Research output: Journal contributions › Journal articles › Research › peer-review

DOI

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