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.

With the use of differential scanning calorimetry (DSC), the characteristic temperatures and enthalpy of phase transformations were

defined for commercial AlSi9Cu3 cast alloy (EN AC-46000) that is being used for example for pressurized castings for automotive

industry. During the heating with the speed of 10oCmin-1

two endothermic effects has been observed. The first appears at the temperature

between 495 oC and 534 oC, and the other between 555 oC and 631 oC. With these reactions the phase transformation enthalpy comes up as

+6 J g-1

and +327 J g-1

. During the cooling with the same speed, three endothermic reactions were observed at the temperatures between

584 oC and 471 oC. The total enthalpy of the transitions is – 348 J g-1

.

Complimentary to the calorimetric research, the structural tests (SEM and EDX) were conducted on light microscope Reichert and on

scanning microscope Hitachi S-4200. As it comes out of that, there are dendrites in the structure of α(Al) solution, as well as the eutectic

(β) silicon crystals, and two types of eutectic mixture: double eutectic α(Al)+β(Si) and compound eutectic α+Al2Cu+β.

Tests concerning EN AC 48000 (AlSi12CuNiMg) alloy phase transition covered (ATD) thermal analysis and (DSC) differential scanning

calorimetry specifying characteristic temperatures and enthalpy of transformations. ATD thermal analysis shows that during cooling there

exist: pre-eutectic crystallization effect of Al9Fe2Si phase, double eutectic and crystallization α(Al)+β(Si) and multi-component eutectic

crystallization. During heating, DSC curve showed endothermic effect connected with melting of the eutectic α(Al)+β(Si) and phases:

Al2Cu, Al3Ni, Mg2Si and Al9Fe2Si being its components. The enthalpy of this transformation constitutes approx. +392 J g-1

. During

freezing of the alloy, DSC curve showed two exothermal reactions. One is most likely connected with crystallization of Al9Fe2Si phase

and the second one comes from freezing of the eutectic α(Al)+β(Si). The enthalpy of this transformation constitutes approx. –340 J g-1

.

Calorimetric test was accompanied by structural test (SEM) conducted with the use of optical microscope Reichert and scanning

microscope Hitachi S-4200. There occurred solution's dendrites α(Al), eutectic silicon crystal (β) and two types of eutectic solution: double

eutectic α(Al)+β(Si) and multi-component eutectic α+AlSiCuNiMg+β.

Liquid Metal Extraction process using molten Mg was carried out to obtain Nd-Mg alloys from Nd based permanent magnets at 900oC for 24 h. with a magnet to magnesium mass ratio of 1:10. Nd was successfully extracted from magnet into Mg resulting in ~4 wt.% Nd-Mg alloy. Nd was recovered from the obtained Nd-Mg alloys based on the difference in their vapor pressures using vacuum distillation. Vacuum distillation experiments were carried out at 800oC under vacuum of 2.67 Pa at various times for the recovery of high purity Nd. Nd having a purity of more than 99% was recovered at distillation time of 120 min and above. The phase transformations of the Nd-Mg alloy during the process, from Mg12Nd to α-Nd, were confirmed as per the phase diagram at different distillation times. Pure Nd was recovered as a result of two step recycling process; Liquid Metal Extraction followed by Vacuum Distillation.

The goal of the research was to analyze the acoustic emission signal recorded during heat treatment. On a special stand, samples prepared from 27MnCrB5-2 steel were tested. The steel samples were heated to 950°C and then cooled continuously in the air. Signals from phase changes occurring during cooling were recorded using the system for registering acoustic emission. As a result of the changes, Widmanstätten ferrite and bainite structures were observed under a scanning microscope. The recorded acoustic emission signal was analyzed and assigned to the appropriate phase transformation with the use of artificial neural networks.

The paper presents an approach to differential equation solutions for the stiff problem. The method of using the classic transformer model to study nonlinear steady states and to determine the current pulses appearing when the transformer is turned on is given. Moreover, the stiffness of nonlinear ordinary differential state equations has to be considered. This paper compares Runge–Kutta implicit methods for the solution of this stiff problem.

Replacing mathematical models with artificial intelligence tools can play an important role in numerical models. This paper analyses the modeling of the hardening process in terms of temperature, phase transformations in the solid state and stresses in the elastic-plastic range. Currently, the use of artificial intelligence tools is increasing, both to make greater generalizations and to reduce possible errors in the numerical simulation process. It is possible to replace the mathematical model of phase transformations in the solid state with an artificial neural network (ANN). Such a substitution requires an ANN network that converts time series (temperature curves) into shares of phase transformations with a small training error. With an insufficient training level of the network, significant differences in stress values will occur due to the existing couplings. Long-Short-Term Memory (LSTM) networks were chosen for the analysis. The paper compares the differences in stress levels with two coupled models using a macroscopic model based on CCT diagram analysis and using the Johnson-Mehl-Avrami-Kolmogorov (JMAK) and Koistinen-Marburger (KM) equations, against the model memorized by the LSTM network. In addition, two levels of network training accuracy were also compared. Considering the results obtained from the model based on LSTM networks, it can be concluded that it is possible to effectively replace the classical model in modeling the phenomena of the heat treatment process.

In-situ observation of the transformation behavior of acicular ferrite in high-strength low-alloy steel using confocal laser scanning microscopy was discussed in terms of nucleation and growth. It is found that acicular ferrite nucleated at dislocations and slip bands in deformed austenite grains introduced by hot deformation in the non-recrystallization austenite region, and then proceeded to grow into an austenite grain boundary. According to an ex-situ EBSD analysis, acicular ferrite had an irregular shape morphology, finer grains with sub-grain boundaries, and higher strain values than those of polygonal ferrite. The fraction of acicular ferrite was affected by the deformation condition and increased with increasing the amount of hot deformation in the non-recrystallization austenite region.