The Shape Recognition in Cold Strip Rolling Based on SVM

Abstract

In this paper, a Multi-Classification SVMs classifier in terms of the theory of SVM is presented and which can tell the various properties of panel surface. The sample data is obtained by preprocessing the data which is measured through the flatness detector in the cold-rolled operation. Using the supervised method of one-class-against-the-rest to train Multi-Classification SVMs classifier. Finally, testing the performance of classifier by test data. The simulation results show that the proposed method performs high recognition rate in processing little sample data and has good ability of generalization. To the flatness recognition, this is a new research method.

Evaluation Method and Application of Cold Rolled Strip Flatness Quality Based on Multi-Objective Decision-Making

“…Li et al [11] propose a kernel extreme learning machine flatness recognition model based on particle swarm optimization. In summary, the popular flatness recognition models are fuzzy [12], support vector machine (SVM) [13], neural network [14], etc. However, regarding the category of flatness defects, in addition to the typical overall flatness pattern, there are various local wave shapes of variable location and size generated during rolling, which can cause defects, such as ribbing in the strip [15,16].…”