1. Digital Intelligence Department, Xinyu Iron and Steel Co., Ltd., Xinyu 338001, Jiangxi, China; 2. National Engineering Technology Research Center for Advanced Rolling Technology and Intelligent Manufacturing, University of Science and Technology Beijing, Beijing 100083, China
Abstract：Surface longitudinal crack is one of the most common surface defects on continuous casting slabs. Due to environmental factors, the on-line detection accuracy of longitudinal cracks on the surface of casting slab is not high, and the quality inspection of casting slab in major steel mills still depends on manual work. Therefore, a method of predicting longitudinal cracks on the surface of casting slab based on particle swarm optimization probabilistic neural network PNN is proposed. Firstly, continuous casting production process tracking and data time-space transformation was established to match the production process data with the slab on length direction. The Bayes minimum risk criterion of PNN was used for supervised feature learning, and the optimization algorithm PSO was used to optimize the selection of key parameters of PNN, and the final model PSO-PNN was obtained. Finally, the quality defect data and production process data of continuous casting line in a steel mill are used for experimental verification. The results show that the classification accuracy of the method is 97.5% for the whole slab and precision and recall for surface longitudinal crack of slab on length direction are above 92%, which can effectively realize the prediction of the longitudinal cracks on the surface of the full length of the slab, and provide a reference for on-site quality inspection personnel.
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2022, Vol. 41 
