A Novel Curve Pattern Recognition Framework for Hot-Rolling Slab Camber

Camber is a typical asymmetrical defect of slabs in the hot-rolling process. The identification of camber plays a key role in improving the quality of the finished strip. To obtain the shape information of the camber, we select the derivative dynamic time warping (DDTW) distance as the measure of curve similarity. An iterative self-organizing data analysis clustering algorithm integrated with DDTW is proposed to divide the sample into different clusters. A curve template is generated via polynomial fitting in each cluster based on the dynamic mechanism of camber. Subsequently, the dynamic time warping (DTW) and DDTW distances are adopted as the input and a weighted random forest (WRF) algorithm is developed for the camber classification to address the sample imbalance problem. Comparative experiments are conducted on the camber sample collected in the actual factory to test the clustering and classification performances. The experimental results indicate that the error rates (ERRs) of four distance-based methodsEuclidean distance, Pearson distance, DTW, and DDTW are 32.7, 28.5, 19.1 and 12.3, respectively, and ERRs of the four classification algorithmsDDTW-SVM, DDTW-KNN, DDTW-RF, and DDTW-WRF are 9.8, 5.7, 4.7, and 2.1, respectively, which verifies the superior performance of the proposed method in this article.