Due to the requirement of magnetic properties of silicon steel sheets, producing high-precision size strips is the main aim of the cold rolling industry. The tapered work roll shifting technique of the six-high cold rolling mill is effective in reducing the difference in transverse thickness of the strip edge, but the effective area is limited, especially for a high crown strip after the hot rolling process. The six-high mill with a small work roll size can produce a strip with higher strength and lower thickness under a smaller rolling load. At the same time, the profile of the strip can be substantially improved. By advancing a well-established analytical method, a series of simulation analyses are conducted to reveal the effectiveness of a small work roll radius for the strip profile in the six-high cold rolling process. Through the analysis of flattening deformation and deflection deformation on the load, the change rule of the strip profile produced by the work roll with a small roll diameter can be obtained. Combined with theoretical analysis and industrial experiments, it can be found that the improvement effect of the small work roll radius on the profile of the silicon strip is as significant.

With the development of Industry 4.0 and the implementation of the 14th Five-Year Plan, intelligent manufacturing has become a significant trend in the steel industry, which can propel the steel industry toward a more intelligent, efficient, and sustainable direction. At present, the operation mode of unmanned warehouse area for slabs and coils has become relatively mature, while the positioning accuracy requirement of bars is getting more stringent because they are stacked in the warehouse area according to the stacking position and transferred by disk crane. Meanwhile, the traditional laser ranging and line scanning method cannot meet the demand for precise positioning of the whole bundle of bars. To deal with the problems above, this paper applies machine vision technology to the unmanned warehouse area of bars, proposing a binocular vision-based measurement method. On the one hand, a 3D reconstruction model with sub-pixel interpolation is established to improve the accuracy of 3D reconstruction in the warehouse area. On the other hand, a feature point matching algorithm based on motion trend constraint is established by means of multi-sensor data fusion, thus improving the accuracy of feature point matching. Finally, a high-precision unmanned 3D reconstruction of the bar stock area is completed.

Equipment wear and assembly clearances can change the longitudinal stiffness of hot strip mills and further affect roll-gap levelling accuracy and asymmetric strip profile control. In this study, the longitudinal stiffness of a 1580 mm four-high hot strip finishing mill was investigated by combining the analytical calculation of the hydraulic press-down system with a three-dimensional mill–strip finite element model. The effects of typical horizontal and vertical gap forms, including work-roll offset, same-side deflection, roll crossing, and unilateral vertical clearance caused by step-pad wear, on total longitudinal stiffness and stiffness difference between the two sides were analysed systematically. The results show that work-roll horizontal offset changes the longitudinal stiffness in a nonlinear manner, whereas work-roll rotation and roll crossing generally reduce the longitudinal stiffness and increase the stiffness asymmetry between the two sides. Unilateral vertical clearance also causes nonlinear variation in both total stiffness and side-to-side stiffness difference. The proposed method was further applied to the stiffness prediction module of the Guangxi BG 1700 mm hot strip mill production line, providing support for equipment maintenance, roll-gap levelling, and stable strip production.