To effectively remove non-metallic inclusions from the steel during the flowing in a five-strand asymmetric tundish, the novel configuration of the impact pad was developed. For analysis, complex numerical modelling in the programme ANSYS Fluent was used. The Lagrangian Discrete Phase Model of inclusion tracking was applied. The distribution of inclusions, with sizes ranging from 2 µm to 100 µm and density from 2500 to 3500 kg·m⁻³, was considered only through the shroud tube. The residence time distribution (RTD) curves and inclusion removal efficiency were used for evaluation of steady state steel flow character depending on internal configuration of a tundish with an impact pad in two design modifications (Modification 1—M1, Modification 2—M2). The preliminary results showed that in the case of asymmetric geometry plays a role the computational mesh independency. The assembly method with cut cell approach was satisfactory even when the tundish geometry was changed. The RTD curves with an M1 showed a huge dead volume in the tundish. In the case with an M2, the RTD curves are more or less uniform for all casting strands, and the removal of inclusions to slag increased from about 55% up to 70% in comparison with M1.

Improving the competitiveness of steel companies is linked to sustainable, quality-compliant steel production. Therefore, new steel production technologies contributing to increased cleanliness of steel are continuously being developed and optimized. One way to achieve a high steel quality is to use electro slag remelting (ESR) technology. In this paper, the principle of ESR technology and the importance of fused slags for optimizing the process are outlined. The aim of this work was to analyze the main thermophysical properties of steel and fused slags used in the ESR process. Determination of the properties of steel and slags was performed using the FactSage calculation software, which involved the calculation of the liquid and solid temperature of steel and slags, the calculation and construction of quaternary diagrams, and the calculation of viscosity. The resulting quaternary diagrams revealed the substantial influence of chemical composition on melting temperatures of slags. In order to validate the acquired results, a CrNiMoV-type steel was subjected to investigation of its metallographic cleanliness and evaluation of its mechanical properties; the ESR process was shown to significantly improve the cleanliness of the steel and improve the mechanical properties of the steel compared to its cleanliness and quality when produced via vacuum degassing (VD) technology. During the ESR process, the average size of non-metallic inclusions was reduced from 20 μm to 10 μm, and the maximum size of non-metallic inclusions was reduced from 50 μm to 28 μm. The mechanical properties of the steel produced using ESR technology were impacted as follows: the ductility increased by 10%, contraction increased by 18%, notched toughness at 20 °C increased by 46%, and at −40 °C (respectively −50 °C) it increased by 30%.

The submitted article deals with the use of physical and numerical modelling to study the process of the steel flow in an asymmetric five-strand tundish that continuously casts steel. For the purposes of physical modelling, a 1:4-scale plexiglass model was used as the operating tundish, and for numerical modelling, the geometry of the operating tundish was created on a 1:1 scale. A model liquid (water) was used in the physical modelling of the melt flow process, while liquid steel was used as the standard flowing medium in the numerical modelling. We assessed the relevant operating parameters influencing the characteristics of the flow of the bath in the tundish—the shape of the turbulence inhibitor, the position of the ladle shroud in relation to the turbulence inhibitor and the distance between the ladle shroud orifice and the bottom of the turbulence inhibitor. The preliminary results show that optimal steel flow characteristic results are achieved by using the TI3-C configuration. The results from both modelling methods achieved the same characteristics, therefore verifying the results of each other and demonstrating that when taken together, the results of physical and numerical modelling can be considered sufficiently informative.

Commercially pure Cu features excellent electric conductivity but low mechanical properties. In order to improve the mechanical properties of Cu, strengthening elements can be added to prepare alloys or composites featuring enhanced performances. This study focuses on the detailed characterization of the microstructure of a Cu composite strengthened with Al₂O₃ particles during high shear strain processing. The Cu-Al₂O₃ mixture was prepared by powder metallurgy and directly consolidated by the intensive plastic deformation method of hot rotary swaging. Samples cut from the consolidated piece were further processed by the severe plastic deformation method of high pressure torsion (HPT). The primary aim was to investigate the effects of varying degrees of the imposed shear strain, i.e., the number of HPT revolutions, microstructure development (grain size and morphology, texture, grain misorientations, etc.) of the consolidated composite; the microstructure observations were supplemented with measurements of Vickers microhardness. The results showed that the added oxide particles effectively hindered the movement of dislocations and aggravated grain fragmentation, which also led to the relatively high presence of grain misorientations pointing to the occurrence of residual stress within the microstructure. The high shear strain imposed into (the peripheral region of) the sample subjected to four HPT revolutions imparted equiaxed ultra-fine grains and an average Vickers microhardness of more than 130 HV0.1.

In engineering practice, it is often necessary to determine functional relationships between dependent and independent variables. These relationships can be highly nonlinear, and classical regression approaches cannot always provide sufficiently reliable solutions. Nevertheless, Machine Learning (ML) techniques, which offer advanced regression tools to address complicated engineering issues, have been developed and widely explored. This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials. The ML-based regression methods of Artificial Neural Networks (ANNs), Support Vector Machine (SVM), Decision Tree Regression (DTR), and Gaussian Process Regression (GPR) are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon steel. Although the GPR method has not been used for such a regression task before, the results showed that its performance is the most favorable and practically unrivaled; neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.

The presented paper deals with the use of physical modelling to study the degassing process of aluminium melts in the refining ladle by blowing inert gas through a rotating impeller. For the purposes of physical modelling, a plexiglass model in a scale of 1:1 is used for the operating ladle. Part of the physical model is a hollow shaft used for gas supply that is equipped with an impeller and two baffles. The degassing process of aluminium melt by blowing of inert gas is simulated at physical modelling by a decrease of dissolved oxygen in the model liquid (water). This paper is aimed at the evaluation of laboratory experiments that were obtained by the method of physical modelling. Attention is focused on the assessment of relevant parameters for the degassing process—rotary impeller speeds, volume flow rate of inert gas, the distance of the impeller from the bottom of the refining ladle, and impeller variant. The preliminary results of physical modelling show that the optimal results of the refining process are achieved by using the F2A 190 impeller.