The invention discloses a weld joint forming prediction method based on a complementary dual-channel
convolutional neural network. Compared with a BP neural network, the
convolutional neural network has the biggest characteristic that the extraction of
molten pool characteristics is not needed, but the extraction of
molten pool characteristic quantity is automatically carried out through a constructed multi-layer
convolution kernel; the
convolutional neural network takes the whole
molten pool image as the input of the model, so that the time consumed for extracting the feature quantity of the molten
pool is saved. Meanwhile, the loss of molten
pool image information is avoided; compared with a common two-channel convolutional neural network
laser welding seam forming prediction method, the method adopts two
convolution modules to extract shallow layer features of the molten
pool image to extract edge lines of the molten pool, and adopts a two-channel strategy, so that the obtained molten pool image features are more sufficient;
laser welding process parameters are introduced by adopting a full-connection module to jointly predict the
welding seam morphology of the T-shaped joint, so that the prediction performance of the model can be further improved.