Closed-loop control of product geometry by using an artificial neural network in incremental sheet forming with active medium
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In: International Journal of Material Forming, Vol. 14, No. 6, 01.11.2021, p. 1319–1335.
Research output: Journal contributions › Journal articles › Research › peer-review
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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 -