Author(s)

Wang Zhao, Jianfeng Yue, Wenji Liu, Haihua Liu

School of Mechanical Engineering, Tiangong University, Tianjin, China.

Weld seam deviation prediction is the key to weld seam tracking control, which is of great significance for realizing welding automation and ensuring welding quality. Aiming at the problem of weld seam deviation prediction in GMAW (gas metal arc welding), a method of weld seam deviation prediction based on arc sound signal is proposed. By analyzing the feature of the arc sound signal waveform, the time domain feature of the arc sound signal is extracted. The wavelet packet analysis method is used to analyze the time-fre- quency domain feature of the arc sound signal, and the wavelet packet energy feature is extracted. The time domain feature and wavelet packet energy feature are used to establish the feature vector, and the BP (back propagation) neural network is used to realize the weld seam deviation prediction. The results show that the method proposed in this paper has a good weld seam deviation prediction effect, with a mean absolute error of 0.234 mm, which provides a new method for GMAW weld seam recognition.

Weld Seam Deviation, GMAW, Arc Sound, BP Neural Network

Zhao, W. , Yue, J. , Liu, W. and Liu, H. (2021) Weld Seam Deviation Prediction of Gas Metal Arc Welding Based on Arc Sound Signal. World Journal of Engineering and Technology, 9, 51-59. doi: 10.4236/wjet.2021.91004.