Possibilities for Renovation of Functional Surfaces of Backup Rolls Used during Steel Making

Abstract

The paper presents the results of the research aimed at possibilities of renovation of functional surfaces of back-up rolls that are part of the hot strip rolling mill operating line using new types of additive materials. Experimentally, four melts of alloyed steel were produced in laboratory conditions from which four types of filler materials in the form of wires were made. They were made by pulling through dies to a diameter of 2.4 mm. The renovation of functional surfaces was performed on the batch of rolls with the outer diameter of 1000 mm and length 3000 mm. Cladding technology submerged arc welding (SAW) was applied for cladding of four spirals with 40 mm oscillation onto the base material of the 31CrMoV9 (EN 10085) sub-roll. The base flux marked WLDC 17-AWS A5.17-89 EM13K with basicity index 3 was used for cladding. The four-layer weld deposition was made to eliminate the effect of mixing the surfacing layers with the base material. The refurbished rolls were annealed after welding. Surface quality was assessed by visual inspection (VT) according to EN ISO 17637. The presence of internal errors was determined by ultrasound control (UT) in accordance with EN ISO 11666 and EN ISO 17640. The quality of the clad layers was analysed on metallographic sections using light microscopy. The microhardness of the individual clad layers was evaluated according to Vickers according to EN ISO 9015-2. Based on realized experiments it can be stated that the selected cladding parameter was suitable, and the overlay layers showed no surface or internal defects. Mechanical and tribological tests were carried out, but they showed significant influence of chemical composition of additive materials on mechanical properties, or wear resistance of the layers.

Recognition of Scratches and Abrasions on Metal Surfaces Using a Classifier Based on a Convolutional Neural Network

“…They are caused by friction (scratching) of the strip against the surface of mechanical equipment. This is especially true for defects formed in the longitudinal direction [2,10,11].…”

Steel Surface Defect Classification Using Deep Residual Neural Network

“…In previous works, the authors analyzed a significant number of defects of metallurgical equipment, systematized the causes of their occurrence, and proposed methods for their prediction [24]. However, the potential of such methods is not fully used, because an increase in the speed of casting on continuous billet casting machines and an increase in the speed of rolling cause new types of damage to metallurgical equipment [25,26]. Equipment failures lead to changes in the geometry of defects that occur in rolled metal, and the "instability" of their parameters.…”