Improved Ultrasonic Dead Zone Detectability of Work Rolls Using a Convolutional Neural Network

Abstract

Rolled carbon steel sheets used in industrial and construction sites are formed by passing metal stock between two rotating rolls using a rolling mill, and the work roll is an essential part of the rolling mill. As the work rolls are in direct contact with the workpiece, the process quality is highly sensitive to their surface integrity, which is maintained through rough and finish cuttings; ultrasonic inspection is often performed after rough cutting the surfaces of work rolls. Ultrasonic inspection signals comprise signals reflected from and below the surface. Depending on the size of the subsurface defects, the thickness of the finish cutting is determined. The signals reflected by defects close to the surface overlap with those from the work roll surface, which is referred to as the ultrasonic dead zone and makes defect detection difficult. Since visual detection of flaws is not possible from signals collected from the dead zone, finish cutting is commonly performed up to the dead zone depth; this requires unnecessary cost and process time, which must be improved. Therefore, in this study a convolutional neural network is used to improve defect detection performance in the ultrasonic dead zone during the inspection of work rolls.