TY - GEN
T1 - Seam Penetration Recognition for GTAW Using Convolutional Neural Network Based on Time-Frequency Image of Arc Sound
AU - Ren, Wenjing
AU - Wen, Guangrui
AU - Liu, Shijie
AU - Yang, Zhe
AU - Xu, Bin
AU - Zhang, Zhifen
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/22
Y1 - 2018/10/22
N2 - Online monitoring and diagnosis of welding quality is essential for intelligent welding manufacturing. The recognition performance of penetration for aluminum alloy in gas tungsten arc welding (GTAW) still needs to be improved to meet the strict industry demands. This paper proposed a novel recognition method, time-frequency image based convolution neural network (TF-CNN), for GTAW penetration recognition. Time-frequency images were calculated from arc sound signals using short time Fourier transform and applied to analyze the non-stationarity of arc sound. The logarithm of time-frequency image was taken to construct the appropriate input matrix of CNN, which was optimized to improve its recognition performance, including the activation function, learning rate and architecture of network. The experimental results show that the proposed TF-CNN achieved an excellent recognition performance with 98.2% recognition accuracy and 0.21 accuracy variance for GTAW seam penetration recognition and outperformed the traditional methods. This paper provides some guidance for the application of CNN to other monitoring signals of intelligent manufacturing.
AB - Online monitoring and diagnosis of welding quality is essential for intelligent welding manufacturing. The recognition performance of penetration for aluminum alloy in gas tungsten arc welding (GTAW) still needs to be improved to meet the strict industry demands. This paper proposed a novel recognition method, time-frequency image based convolution neural network (TF-CNN), for GTAW penetration recognition. Time-frequency images were calculated from arc sound signals using short time Fourier transform and applied to analyze the non-stationarity of arc sound. The logarithm of time-frequency image was taken to construct the appropriate input matrix of CNN, which was optimized to improve its recognition performance, including the activation function, learning rate and architecture of network. The experimental results show that the proposed TF-CNN achieved an excellent recognition performance with 98.2% recognition accuracy and 0.21 accuracy variance for GTAW seam penetration recognition and outperformed the traditional methods. This paper provides some guidance for the application of CNN to other monitoring signals of intelligent manufacturing.
KW - Arc sound
KW - convolution neural network
KW - intelligent welding
KW - penetration recognition
KW - time-frequency analysis
UR - https://www.scopus.com/pages/publications/85057249649
U2 - 10.1109/ETFA.2018.8502478
DO - 10.1109/ETFA.2018.8502478
M3 - 会议稿件
AN - SCOPUS:85057249649
T3 - IEEE International Conference on Emerging Technologies and Factory Automation, ETFA
SP - 853
EP - 860
BT - Proceedings - 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation, ETFA 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2018
Y2 - 4 September 2018 through 7 September 2018
ER -