Product quality prediction model based on generative adversarial network and hard case mining

According to the characteristics of process industries, the issue of low recall in identifying defective prod-ucts caused by imbalanced dass was addressed. To extract effective features from high-dimensional data, the advan-tages of one dass F-score and mRMR in feature extraction were combined to effectively reduce the feature dimension and extract valuable features. Then, the Wasserstein Generative Adversarial Network (WGAN) algorithm was em-ployed to augment the quantity of defective product. Subsequently, the focal loss function was optimized with dass weights to enhance the recognition rate of hard case. Furthermore, leveraging the LightGBM algorithm in conjunction with a threshold movement strategy, a quality prediction model was constructed based on WGAN and hard case mining techniques. Finally, the proposed model was applied to the open-source SECOM dataset, and the result indicated that the presented approach effectively enhanced the recall rate of defective products while maintai-ning Overall accuracy, which provided a scientific and practical method for in-depth exploration of the intricate map-ping relationship between critical production factors and product quality, as well as facilitating intelligent quality prediction efforts. © 2024 CIMS. All rights reserved.