Longitudinal Crack Detection Approach Based on Principal Component Analysis and Support Vector Machine for Slab Continuous Casting

Abstract

Longitudinal cracks are the typical surface defects of continuous casting slabs, resulting in additional processing or even casting interruption. Monitoring longitudinal crack defects is of great significance in stabilizing and improving the slab quality. Herein, a monitoring model is developed to recognize the longitudinal crack defect of continuous casting slabs using principal component analysis (PCA) and support vector machine (SVM). First, the typical characteristics of the temperature patterns corresponding to the longitudinal crack defect are extracted, including the normal casting temperature with small and large fluctuations, as well as the longitudinal crack temperature. Then, PCA is used to reduce the dimension of these characteristics for removing the redundancy and reducing the computational burden. Subsequently, an SVM is applied to identify normal and longitudinal crack temperature patterns for training the monitoring model PCA–SVM. The monitoring performance is verified by the test data, in which the training accuracy and test accuracy are 100% and 96%, respectively. It is worth mentioning that the model can successfully predict all the real longitudinal crack defects, showing excellent detection performance. The established model is expected to provide a theoretical basis and a reliable way for online monitoring of slab surface cracks.