Degradation by the penetration of oxidation into the Cr12 roller steel is evaluated during thermal fatigue tests in the laboratory in the temperature range of 500–700 °C. A qualitative assessment is carried out with regard to the thermal load, the microstructure and the test temperature. The results show that the specific properties of the microstructure with respect to thermal stress and temperature have a significant influence on the oxidation behavior as well as on the crack propagation mode and crack growth. The conditions that lead to an increase in the oxidation rate and thus to premature and sudden local chipping of the roll surface layer are analyzed and explained.

The aim of this study was to investigate the surface deterioration of high-Cr roll steel caused by the spalling of larger particles during thermal fatigue. The mechanisms of surface deterioration due to spalling of larger particles are discussed. Using a laboratory thermal fatigue test that replicates hot rolling conditions, samples were tested cyclically (up to 4500 times) at maximum cycle temperatures of 500, 600 and 700 °C, followed by water cooling. Specimens with surface deterioration were selected for analysis, revealing important influencing parameters, i.e., the combination of test temperatures, chemical composition, thermal stress and microstructural properties, leading to oxidation-assisted crack growth in different directions and consequent surface deterioration due to early spalling of larger particles. Here, we describe the mechanisms of crack propagation, especially in the lateral direction, and their relation to the subsequent spalling of larger particles, which depend on the influence of the local chemical composition on the microstructural constituents, as well as their distribution and properties. The results obtained in this study can be used in the development of roll steel microstructures with improved resistance to the identified mechanisms of surface degradation.

In this paper, the Conditional Average Estimator artificial neural network (CAE ANN) was used to analyze the influence of chemical composition in conjunction with selected process parameters on the yield strength and elongation of an extruded 6082 aluminum alloy (AA6082) profile. Analysis focused on the optimization of mechanical properties as a function of casting temperature, casting speed, addition rate of alloy wire, ram speed, extrusion ratio, and number of extrusion strands on one side, and different contents of chemical elements, i.e., Si, Mn, Mg, and Fe, on the other side. The obtained results revealed very complex non-linear relationships between all of these parameters. Using the proposed approach, it was possible to identify the combinations of chemical composition and process parameters as well as their values for a simultaneous increase of yield strength and elongation of extruded profiles. These results are a contribution of the presented study in comparison with published research results of similar studies in this field. Application of the proposed approach, either in the research and/or in industrial aluminum production, suggests a further increase in the relevant mechanical properties.

In the present contribution two tests for thermal fatigue testing, which have been developed in our group, are presented. First test has provided internal cooling system of sample, while second has external cooling. For both tests heating and cooling of samples are computer guided that enables very reliable results of testing. The first test is more appropriate for testing the base material, i.e. roll cast irons, roll steels, tool steels. The second test is more appropriate for experiments that are aimed for selection of appropriate tool surface treatment, i.e. laser cladding, nitriding, coating, etc., and to compare and to achieve improved resistance against thermal fatigue of produced surface layers.

The paper presents a model for predicting the roll wear in the hot rolling process. It includes all indicators from the entire continuous rolling line that best predict the roll wear in the hot rolling process. Data for model development were obtained from annual production on the first rolling stand of the continuous roll mill. The main goal of the research was to determine significant parameters that affect the wear of the roll in the process of hot rolling. It has been found that the amount of rolled material before the re-machining of the roll surface has the greatest impact on the life of the roll contour. Therefore, the amount of material rolled before re-machining of the roll was used to estimate the wear of the roll. An artificial neural network was used to predict this amount of rolled material and was validated using data from one-year production.