The paper presents the test description and results of thermal bowing of RC beams exposed to non-uniform heating at high temperature. Bending of a non-uniformly heated element is caused by free thermal elongation of the material it is made of. The higher the temperature gradient, the greater the bending. In the case when an element is exposed to load and high temperature simultaneously, apart from free bending also deformation of the RC element may occur, which is caused by the decrease of the concrete or reinforcing steel mechanical properties. In order to examine the contribution of the deflection caused by thermal bowing to the total deformation of the bent element with a heated tension zone, an experimental study of freely heated (unloaded) beams was performed. RC beams were heated: (1) on three sides of the cross-section or (2) only on the bottom side. Deflection of elements loaded by a substitute temperature gradient was calculated using the Maxwell-Mohr formula. The test results show that deflection of freely heated RC beams (caused by the thermal bowing phenomenon) can be 10 to 20% of the total deflection of loaded RC beams with a heated tension zone.

A mathematical model of austenite - bainite transformation in austempered ductile cast iron has been presented. The model is based on a model developed by Bhadeshia [1, 2] for modelling the bainitic transformation in high-silicon steels with inhibited carbide precipitation. A computer program has been developed that calculates the incubation time, the transformation time at a preset temperature, the TTT diagram and carbon content in unreacted austenite as a function of temperature. Additionally, the program has been provided with a module calculating the free energy of austenite and ferrite as well as the maximum driving force of transformation. Model validation was based on the experimental research and literature data. Experimental studies included the determination of austenite grain size, plotting the TTT diagram and analysis of the effect of heat treatment parameters on the microstructure of ductile iron. The obtained results show a relatively good compatibility between the theoretical calculations and experimental studies. Using the developed program it was possible to examine the effect of austenite grain size on the rate of transformation.

The article discusses the development of an approximation model of selected plastic and mechanical properties obtained from compression tests of model materials used in physical modeling. The use of physical modeling with the use of soft model materials such as a synthetic wax branch with various modifiers is a popular tool used as an alternative or verification of numerical modeling of bulk metal forming processes. In order to develop an algorithm to facilitate the choice of material model to simulate the behavior of real-metallic materials used in industrial production processes the induction of decision trees was used. First of all, the Statistica program was used for data mining, which made it possible to determine / find the relationship between the percentage of particular constituents of the model material (base material and modifiers) and yield strength, critical and maximum strain, and provide the opportunity to indicate the most important variables determining the shape of the stress – strain curve. Next, using the induction of decision trees, an approximation model was developed, which allowed to create an algorithm facilitating the selection of individual modifying components. The last stage of the research was verification of the correctness of the developed algorithm. The obtained research results indicate the possibility of using decision tree induction to approximate selected properties of modeling materials simulating the behavior of real materials, thus eliminating the need for costly and time-consuming experiments carried out on metallic material.

The paper presents a multi-scale mathematical model dedicated to a comprehensive simulation of resistance heating combined with the melting and controlled cooling of steel samples. Experiments in order to verify the formulated numerical model were performed using a Gleeble 3800 thermo-mechanical simulator. The model for the macro scale was based upon the solution of Fourier-Kirchhoff equation as regards predicting the distribution of temperature fields within the volume of the sample. The macro scale solution is complemented by a functional model generating voluminal heat sources, resulting from the electric current flowing through the sample. The model for the micro-scale, concerning the grain growth simulation, is based upon the probabilistic Monte Carlo algorithm, and on the minimization of the system energy. The model takes into account the forming mushy zone, where grains degrade at the melting stage – it is a unique feature of the micro-solution. The solution domains are coupled by the interpolation of node temperatures of the finite element mesh (the macro model) onto the Monte Carlo cells (micro model). The paper is complemented with examples of resistance heating results and macro- and micro-structural tests, along with test computations concerning the estimation of the range of zones with diverse dynamics of grain growth.

The article presents the use of computer graphics methods and computational geometry for the analysis on changes of geometrical parameters for a mixed zone in resistance-heated samples. To perform the physical simulation series of resistance heating process, the Gleeble 3800 physical simulator, located in the Institute for Ferrous Metallurgy in Gliwice, was used. The paper presents a description of the test stand and the method for performing the experiment. The numerical model is based on the Fourier-Kirchoff differential equation for unsteady heat flow with an internal volumetric heat source. In the case of direct heating of the sample, geometrical parameters of the remelting zone change rapidly. The described methodology of using shape descriptors to characterise the studied zone during the process allows to parametrise the heat influence zones. The shape descriptors were used for the chosen for characteristic timing steps of the simulation, which allowed the authors to describe the changes of the studied parameters as a function of temperature. Additionally, to determine the impact of external factors, the remelting zone parameters were estimated for two types of grips holding the sample, so-called hot grips of a shorter contact area with the sample, and so-called cold grips. Based on the collected data, conclusions were drawn on the impact of the process parameters on the localisation and shape of the mushy zone.

This article discusses the results of studies using the developed artificial neural networks in the analysis of the occurrence of the four main mechanisms destroying the selected forging tools subjected to five different surface treatment variants (nitrided layer, pad welded layer and three hybrid layers, i.e. AlCrTiSiN, Cr/CrN and Cr/AlCrTiN). Knowledge of the forging tool durability, needed in the process of artificial neural network training, was included in the set of training data (about 800 records) derived from long-term comprehensive research carried out under industrial conditions. Based on this set, neural networks with different architectures were developed and the results concerning the intensity of the occurrence of thermal-mechanical fatigue, abrasive wear, mechanical fatigue and plastic deformation were generated for each type of the applied treatment relative to the number of forgings, pressure, friction path and temperature.