Closed-loop control of product geometry by using an artificial neural network in incremental sheet forming with active medium

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Closed-loop control of product geometry by using an artificial neural network in incremental sheet forming with active medium. / Thiery, Sebastian; Zein El Abdine, Mazhar; Heger, Jens et al.
In: International Journal of Material Forming, Vol. 14, No. 6, 01.11.2021, p. 1319–1335.

Research output: Journal contributionsJournal articlesResearchpeer-review

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@article{0f1cd5d3691f47a1bf307664ef23a698,
title = "Closed-loop control of product geometry by using an artificial neural network in incremental sheet forming with active medium",
abstract = "A strategy to adjust the product geometry autonomously through an online control of the manufacturing process in incremental sheet forming with active medium is presented. An axial force sensor and a laser distance sensor are integrated into the process setup to measure the forming force and the product height, respectively. Experiments are conducted to estimate the bulging behavior for different pre-determined tool paths. An artificial neural network is consequently trained based on the experimental data to continuously predict the pressure levels required to control the final product height. The predicted pressure is part of a closed-loop control to improve the geometrical accuracy of formed parts. Finally, experiments were conducted to verify the results, where truncated cones with different dimensions were formed with and without the closed-loop control. The results indicate that this strategy enhances the geometrical accuracy of the parts and can potentially be expanded to be implemented for different types of material and geometries.",
keywords = "Engineering, Incremental sheet forming with active medium, closed-loop control, artifical neural networks, geometrical accuracy",
author = "Sebastian Thiery and {Zein El Abdine}, Mazhar and Jens Heger and {Ben Khalifa}, Noomane",
note = "Publisher Copyright: {\textcopyright} 2020, The Author(s).",
year = "2021",
month = nov,
day = "1",
doi = "10.1007/s12289-020-01598-1",
language = "English",
volume = "14",
pages = "1319–1335",
journal = "International Journal of Material Forming",
issn = "1960-6206",
publisher = "Springer Paris",
number = "6",

}

RIS

TY - JOUR

T1 - Closed-loop control of product geometry by using an artificial neural network in incremental sheet forming with active medium

AU - Thiery, Sebastian

AU - Zein El Abdine, Mazhar

AU - Heger, Jens

AU - Ben Khalifa, Noomane

N1 - Publisher Copyright: © 2020, The Author(s).

PY - 2021/11/1

Y1 - 2021/11/1

N2 - A strategy to adjust the product geometry autonomously through an online control of the manufacturing process in incremental sheet forming with active medium is presented. An axial force sensor and a laser distance sensor are integrated into the process setup to measure the forming force and the product height, respectively. Experiments are conducted to estimate the bulging behavior for different pre-determined tool paths. An artificial neural network is consequently trained based on the experimental data to continuously predict the pressure levels required to control the final product height. The predicted pressure is part of a closed-loop control to improve the geometrical accuracy of formed parts. Finally, experiments were conducted to verify the results, where truncated cones with different dimensions were formed with and without the closed-loop control. The results indicate that this strategy enhances the geometrical accuracy of the parts and can potentially be expanded to be implemented for different types of material and geometries.

AB - A strategy to adjust the product geometry autonomously through an online control of the manufacturing process in incremental sheet forming with active medium is presented. An axial force sensor and a laser distance sensor are integrated into the process setup to measure the forming force and the product height, respectively. Experiments are conducted to estimate the bulging behavior for different pre-determined tool paths. An artificial neural network is consequently trained based on the experimental data to continuously predict the pressure levels required to control the final product height. The predicted pressure is part of a closed-loop control to improve the geometrical accuracy of formed parts. Finally, experiments were conducted to verify the results, where truncated cones with different dimensions were formed with and without the closed-loop control. The results indicate that this strategy enhances the geometrical accuracy of the parts and can potentially be expanded to be implemented for different types of material and geometries.

KW - Engineering

KW - Incremental sheet forming with active medium

KW - closed-loop control

KW - artifical neural networks

KW - geometrical accuracy

UR - http://www.scopus.com/inward/record.url?scp=85095941285&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/ccdb828f-4bbc-33a1-b1bf-5406ac5b08bf/

U2 - 10.1007/s12289-020-01598-1

DO - 10.1007/s12289-020-01598-1

M3 - Journal articles

VL - 14

SP - 1319

EP - 1335

JO - International Journal of Material Forming

JF - International Journal of Material Forming

SN - 1960-6206

IS - 6

ER -

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