摘要
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</span><span style="font-family:Verdana;"> </span><span style="font-family:Verdana;">(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</span><span style="font-family:Verdana;">- </span><span style="font-family:Verdana;">quency domain feature of the arc sound signal, and the wavelet packet energy feature </span><span style="font-family:Verdana;">is</span><span style="font-family:Verdana;"> 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</span><span style="font-family:Verdana;"> </span><span style="font-family:Verdana;">mm, which provides a new method for GMAW weld seam recognition.
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</span><span style="font-family:Verdana;"> </span><span style="font-family:Verdana;">(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</span><span style="font-family:Verdana;">- </span><span style="font-family:Verdana;">quency domain feature of the arc sound signal, and the wavelet packet energy feature </span><span style="font-family:Verdana;">is</span><span style="font-family:Verdana;"> 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</span><span style="font-family:Verdana;"> </span><span style="font-family:Verdana;">mm, which provides a new method for GMAW weld seam recognition.
