The invention provides an asymmetric self-supervised
molten pool defect identification method based on feature redundancy loss and a related
system, and belongs to the field of industrial
quality monitoring and defect identification. By performing a view enhancement strategy on a
label-free
molten pool data set, the learning invariance and feature distinguishing capabilities of the model are enhanced; a self-
supervised learning network architecture is introduced, and low-dimensional representation is extracted from the enhanced view of the
molten pool image; feature redundancy loss is introduced to serve as a target
loss function, redundancy between components of the vectors is reduced to the maximum extent, independence of component representation is improved,
coupling between component representation is relieved, and correct convergence of network learning is guaranteed. According to the method,
invariant feature knowledge of various
label-free molten
pool images can be learned, all useful information is fully utilized, identification based on the molten
pool defect state is realized, and single molten
pool image health state evaluation with strong indication and high precision can be output.