A single sensor is used to obtain welding information in welding monitoring process, but this method has some shortcomings. In order to obtain more comprehensive and reliable welding information, this paper designed a...A single sensor is used to obtain welding information in welding monitoring process, but this method has some shortcomings. In order to obtain more comprehensive and reliable welding information, this paper designed and built a welding multi-information wireless monitoring system with STM32-F407ZET6 as the control core and ALK8266 as the wireless transmission module. Real-time acquisition, transmission and display of electric arc signal and welding image information are realized in the monitoring system. This paper mainly introduces the hardware and software core of the monitoring system. At the same time, the signal collected by the monitoring system is compared with the original signal, and the accuracy of the remote monitoring system is tested. The monitoring system is used in welding test. The test results show that the accuracy of the monitoring system meets the requirements, and the on-line monitoring of electric arc signal and welding image can be realized in the welding process.展开更多
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 predi...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.展开更多
文摘A single sensor is used to obtain welding information in welding monitoring process, but this method has some shortcomings. In order to obtain more comprehensive and reliable welding information, this paper designed and built a welding multi-information wireless monitoring system with STM32-F407ZET6 as the control core and ALK8266 as the wireless transmission module. Real-time acquisition, transmission and display of electric arc signal and welding image information are realized in the monitoring system. This paper mainly introduces the hardware and software core of the monitoring system. At the same time, the signal collected by the monitoring system is compared with the original signal, and the accuracy of the remote monitoring system is tested. The monitoring system is used in welding test. The test results show that the accuracy of the monitoring system meets the requirements, and the on-line monitoring of electric arc signal and welding image can be realized in the welding process.
文摘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.
