A study on spot welding quality judgment of stainless steel plate based on semi-supervised conditional generation adversarial network

Abstract

During RSW, the number of qualified samples is much more than the unqualified ones, forming an unbalanced set, thus affecting the training effect of the model, meanwhile, most samples are unlabeled, and if all the joints are marked, it is more expensive. Based on this, a method of spot welding quality judgment of stainless steel plate based on semi-supervised conditional generation adversarial network is proposed. Firstly, labels are added to the noise to generate labeled data and unlabeled data, which are mixed in a certain proportion to ensure the diversity of generated data. Then, the real data is divided into two parts, in which the unlabeled part plays a game with the generated data to generate samples as close to the real as possible, meanwhile, the data with true discriminant results and labeled data are input into the autoencoder to obtain the feature vectors set of different states. Finally, the training parameters and test samples are input into the classifier to obtain the judgment results. The proposed method was applied to application case, and the results showed that it not only had a good fitting effect, but also had a high classification accuracy. Consequently, the method proposed was effective.