TY - GEN
T1 - A Modular Edge-/Cloud-Solution for Automated Error Detection of Industrial Hairpin Weldings using Convolutional Neural Networks
AU - Vater, Johannes
AU - Schlaak, Pascal
AU - Knoll, Alois
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - The traction battery and the electric motor are the most important components of the electrified powertrain. To increase the energy efficiency of the electric motor, wound copper wires are being replaced by coated rectangular copper wires, so-called hairpins. Hence, to connect the hairpins conductively, they must be welded together. However, such new production processes are unknown compared with classic motor production. Therefore, this research aims to integrate Industry 4.0 techniques, such as cloud and edge computing, and advanced data analysis in the production process to better understand and optimize the manufacturing processes. Welding defects are classified with the help of a convolutional neural network (CNN) (predictive analysis) and, depending on the defect, a recommended course of action for reworking (prescriptive analysis) is given. However, the application of such complex algorithms as neural networks to large amounts of data requires huge computing resources. Therefore, a modular combination of an edge and cloud architecture is proposed in this paper. Furthermore, a pure cloud solution is compared with the edge solution.
AB - The traction battery and the electric motor are the most important components of the electrified powertrain. To increase the energy efficiency of the electric motor, wound copper wires are being replaced by coated rectangular copper wires, so-called hairpins. Hence, to connect the hairpins conductively, they must be welded together. However, such new production processes are unknown compared with classic motor production. Therefore, this research aims to integrate Industry 4.0 techniques, such as cloud and edge computing, and advanced data analysis in the production process to better understand and optimize the manufacturing processes. Welding defects are classified with the help of a convolutional neural network (CNN) (predictive analysis) and, depending on the defect, a recommended course of action for reworking (prescriptive analysis) is given. However, the application of such complex algorithms as neural networks to large amounts of data requires huge computing resources. Therefore, a modular combination of an edge and cloud architecture is proposed in this paper. Furthermore, a pure cloud solution is compared with the edge solution.
KW - cloud computing
KW - convolutional neural network
KW - edge computing
KW - electric motor
KW - hairpin
KW - machine learning
KW - predictive analytics
KW - prescriptive analytics
UR - http://www.scopus.com/inward/record.url?scp=85094174472&partnerID=8YFLogxK
U2 - 10.1109/COMPSAC48688.2020.0-202
DO - 10.1109/COMPSAC48688.2020.0-202
M3 - Conference contribution
AN - SCOPUS:85094174472
T3 - Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020
SP - 505
EP - 510
BT - Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020
A2 - Chan, W. K.
A2 - Claycomb, Bill
A2 - Takakura, Hiroki
A2 - Yang, Ji-Jiang
A2 - Teranishi, Yuuichi
A2 - Towey, Dave
A2 - Segura, Sergio
A2 - Shahriar, Hossain
A2 - Reisman, Sorel
A2 - Ahamed, Sheikh Iqbal
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 44th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2020
Y2 - 13 July 2020 through 17 July 2020
ER -
