The paper presents the results of work leading to the construction of a spatial hybrid model based on finite element (FE) and Monte Carlo (MC) methods allowing the computer simulation of physical phenomena accompanying the steel sample testing at temperatures that are characteristic for soft-reduction process. The proposed solution includes local density variations at the level of mechanical solution (the incompressibility condition was replaced with the condition of mass conservation), and at the same time simulates the grain growth in a comprehensive resistance heating process combined with a local remelting followed by free/controlled cooling of the sample tested. Simulation of grain growth in the entire computing domain would not be possible without the support of GPU processors. There was a 59-fold increase in the computing speed on the GPU compared to single-threaded computing on the CPU. The study was complemented by examples of experimental and computer simulation results, showing the correctness of the adopted model assumptions.

Microalloying elements such as Ti, Nb, V, entered into steel they influence their microstructure and mechanical properties, because formation of carbonitrides, M(C,N). Influence of carbonitrides to the microstructure and mechanical properties depends on their basic stereological parameters: volume fraction, Vv, and their size, r. In this work the Cellular Automata model of the kinetics of the carbonitrides precipitation which enable to predict the image of the microstructure and calculate the size of carbonitrides formed during isothermal annealing of supersaturated steel is presented. In the high temperature the microalloying elements inhibit the austenite grains growth. Chemical composition of steel has influence to volume fraction and size of precipitations. The work is supplemented with examples of experimental comparison.

This paper presents an innovative solution in the form of a virtual reality (VR) and high performance computing (HPC) system dedicated to aid designing rotary forming processes with laser beam reheating the material formed. The invented method allowing a virtual machine copy to be coupled with its actual counterpart and a computing engine utilizing GPU processors of graphic NVidia cards to accelerate computing are discussed. The completed experiments and simulations of the 316L stainless steel semi-product spinning process showed that the developed VR-HPC system solution allows the manufacturing process to be effectively engineered and controlled in industrial conditions.