A Smart Roll Wear Check Scheme for Ensuring the Rolling Quality of Steel Plates

Abstract

Roll surface wear morphology directly affects the surface quality of steel plates and even affects the texture composition of plates and strip steel products. Using image processing methods to judge the wear state of a roll is low cost, easy to operate, and easy to realize an automatic smart data processing system. In this paper, we propose a Smart Roll Wear Check (SRWC) scheme for ensuring the rolling quality of steel plates. In the SRWC scheme, roll surface images in different wear stages are analyzed, from which seventeen dimension features are extracted. At the same time, the fractal theory is introduced to explore the relationship between fractal dimensions and roll wear degree. The results show that four characteristic parameters, such as roundness, equivalent area circle radius, second moment and texture entropy, and the fractal dimension can be used as effective parameters to quantitatively judge roll wear state. Lastly, a back-propagation (BP) neural network model for recognition and judgment for roll wear is established. It provides an experimental test to show that the five parameters as a quantitative evaluation for roll wear morphology are effective. By processing the data on the images, the SRWC scheme can demonstrate whether the roll needs to get off mill in time, so as to avoid the hidden danger of safety and ensure the rolling quality of the steel plate.