The invention discloses a scanning
galvanometer laser welding keyhole molten pool automatic identification method based on
deep learning, which comprises the following steps: acquiring original visual image information in a scanning
galvanometer laser welding process, manufacturing a small amount of segmentation mark samples, and forming a semantic segmentation
data set together with the original visual image; and then through training and
verification, obtaining a semi-supervised generative adversarial semantic segmentation model capable of accurately segmenting an optimal attention mechanism of the
keyhole molten pool. A segmentation model is used for segmenting an original visual image, then segmentation images containing a
keyhole and a
molten pool are obtained through traditional
image processing algorithms including image channel segmentation based on pixel values, image
morphological processing denoising, contour searching and the like, and then the segmentation images are multiplied by an original visual
signal for
mask operation, so that in the
welding process, the key hole and the molten
pool are obtained. And accurately identifying a keyhole and a molten
pool. According to the method, the recognition accuracy of dynamic changes of the keyhole and the molten
pool in the scanning
galvanometer laser welding process can be improved, and it is guaranteed that the quality of machined products is controllable.