Deep Learning Based Detection and Width Extraction of Back Molten Pool in TIG Welding

To ensure the real-time and accuracy of pool information extraction in welding process, and to solve the problems of weak anti-interference of traditional image processing algorithms in the field of welding monitoring, poor reliability of real-time monitoring and low degree of automation, and to enable the system to extract and analyze the pool width information in real-time, image processing algorithm and deep learning algorithm were combined in this paper. Through observation of TIG welding pool and detection of penetration information, the reverse pool was divided into three categories. First, the burning pool was screened out by image processing method, and then the incomplete and penetration were classified by a convolution neural network trained by large data samples, which was different from the existing research. Not only good penetration test results were obtained, but also the maximum width of the molten pool was found, and the real-time performance was guaranteed. The results meet the requirements of engineering application.