TY - GEN
T1 - Closing the loop
T2 - 2020 International Conference on Omni-layer Intelligent Systems, COINS 2020
AU - Vater, Johannes
AU - Kirschning, Maximililian
AU - Knoll, Alois
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/8
Y1 - 2020/8
N2 - One challenge faced by the automotive industry is the shift from combustion to electrically powered vehicles. This change strongly impacts on components such as the electric motor and the battery, and hence on production. In this context, the low level of expert knowledge is especially problematic. To meet these new challenges, this paper introduces a data-driven optimization of the production process by integrating a modular edge and cloud computing layer, and advanced data analysis. Defects are classified by a convolutional neural network (CNN) (predictive analytics) and corrected (depending on the defect type) by an automated rework (prescriptive analytics). The architecture of the CNN achieves an accuracy of 99.21% to predict the defect class. The automated rework process is selected through an implemented decision tree. The edge device communicates with a programmable logic controller (PLC) through a cyber physical interface. As an example of its practical application, the method is applied to hairpin welding of the stator of an electric motor with real production data.
AB - One challenge faced by the automotive industry is the shift from combustion to electrically powered vehicles. This change strongly impacts on components such as the electric motor and the battery, and hence on production. In this context, the low level of expert knowledge is especially problematic. To meet these new challenges, this paper introduces a data-driven optimization of the production process by integrating a modular edge and cloud computing layer, and advanced data analysis. Defects are classified by a convolutional neural network (CNN) (predictive analytics) and corrected (depending on the defect type) by an automated rework (prescriptive analytics). The architecture of the CNN achieves an accuracy of 99.21% to predict the defect class. The automated rework process is selected through an implemented decision tree. The edge device communicates with a programmable logic controller (PLC) through a cyber physical interface. As an example of its practical application, the method is applied to hairpin welding of the stator of an electric motor with real production data.
KW - cloud computing
KW - convolutional neural networks
KW - edge computing
KW - electric motors
KW - hairpin
KW - industry 4.0
KW - machine learning
KW - predictive analytics
KW - prescriptive analytics
KW - prescriptive automation
UR - http://www.scopus.com/inward/record.url?scp=85092221535&partnerID=8YFLogxK
U2 - 10.1109/COINS49042.2020.9191386
DO - 10.1109/COINS49042.2020.9191386
M3 - Conference contribution
AN - SCOPUS:85092221535
T3 - 2020 International Conference on Omni-Layer Intelligent Systems, COINS 2020
BT - 2020 International Conference on Omni-Layer Intelligent Systems, COINS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 31 August 2020 through 2 September 2020
ER -