The real-time vision measurement of multi geometric information of the rolled ring in the hot ring rolling process based on deep learning

Abstract

Abstract Hot ring rolling (HRR) is an advanced incremental metal-forming technology to manufacture high-performance rings. Nowadays, the mechanical or laser-sensors measurement methods can only obtain one value of the dimensions on the ring circumference per time unit, the measurement data is single and the information is limited. This paper presents a real-time vision measurement method for multi geometric information of the rolled ring such as ring’s diameter, center position, circularity and growth speed in HRR process. Firstly, the deep learning model is constructed to intelligently and quickly identify rolled ring targets from complex image backgrounds, then the adaptive linear gray scale transformation algorithm is proposed to adjust the gray scale contrast of the image according to changes in ring temperature, the interference of oxide scales and flying chips are eliminated by morphology operation. Finally, the least square method is used to fit the contour points, and further the rolled ring’s geometric information and corresponding variation trend are calculated. Several experiments are conducted on a vertical HRR mill, the geometric information of the rolled ring during the HRR is measured in real-time under the interference of harsh working conditions. For the ring with outer diameter of 350 mm, the measurement error of ring’s outer and inner diameter is less than 0.8 mm, the average processing time per image takes about 80 ms. The research provides a basis for accurate measurement of HRR process.