The use of modern casting materials allows the achievement of higher product quality indices. The conducted experimental studies of new materials allow obtaining alloys with high performance properties while maintaining low production costs. Studies have shown that in certain areas of applications, the expensive to manufacture austempered ductile iron (ADI) can be replaced with ausferritic ductile iron or bainitic nodular cast iron with carbides, obtained without the heat treatment of castings. The dissemination of experimental results is possible through the use of information technologies and building applications that automatically compare the properties of materials, as the machine learning tools in comparative analysis of the properties of materials, in particular ADI and nodular cast iron with carbides.

This paper is devoted to the possibility of increasing the mechanical properties (tensile strength, yield strength, elongation and hardness) of high pressure die casting (HPDC) hypoeutectic Al-Si alloys by high melting point elements: chromium, molybdenum, vanadium and tungsten. EN AC-46000 alloy was used as a base alloy. The paper presents the effect of Cr, Mo, V and W on the crystallization process and the microstructure of HPDC aluminum alloy as well as an alloy from the shell mold. Thermal and derivative analysis was used to study the crystallization process. The possibility of increasing the mechanical properties of HPDC hypoeutectic alloy by addition of high-melting point elements has been demonstrated.

Intelligent computing tools such as artificial neural network and fuzzy logic are used as predictive modeling tools. The use of these methods, combined with model experimental results, may be an excellent predictive tool, allowing us to forecast the microstructure of the tested cast iron at the level of computer simulation. In this study, the reference training cases collected in one database were used to determine the parameters of the neuro-fuzzy ANFIS model. They mainly include the results of observations and measurements of the content of individual microstructural constituents of the compacted graphite iron, examined as a function of the content of individual alloy additives (molybdenum, nickel and copper introduced in various proportions). The training process of such a fuzzy inference system is done by constantly changing its parameters (parameters of the membership function) and determining new rule conclusions as a result of presenting individual case examples from the training sample. The conducted research has shown the possibility of applying the ANFIS model as a tool to control the chemical composition of compacted graphite iron in the production of castings with high-strength parameters.

The application of data mining techniques in the design of modern foundry materials allows achieving higher product quality indicators. Designing of a new product always requires thorough knowledge of the effect of alloying elements on the microstructure and hence also on the properties of the examined material. The conducted experimental studies allow for a qualitative assessment of the indicated relationships, but it is the use of intelligent computational techniques that enables building an approximation model of the microstructure and, owing to this, make predictions with high precision. The developed model of prediction supports the technology-related decisions as early as at the stage of casting design and is considered the first step in selecting the type of material used.

The paper presents the results of microstructure tests of EN AC-46000 hypoeutectic Al–Si alloy with and without high-melting-point elements: chromium, molybdenum, vanadium, and tungsten. The above-mentioned elements were used individually or simultaneously in various combinations. The tested castings were made using two technologies: shell molding and high pressure die casting (HPDC). Using X-ray diffraction and microanalysis of the chemical composition an attempt to determine the phase structure of the tested alloy was made. It has been shown that the microstructure of the base alloy consists of dendrites of α(Al) solid solution and complex eutectic mixtures: ternary α(Al) + Al₁₅(Fe,Mn)₃Si₂ + β(Si) and quaternary α(Al) + Al₂Cu + AlSiCuFeMgMnNi + β(Si). High-melting point elements, regardless of the combination used, attach mainly to intermetallic phases rich in Fe and form the Al₁₅(Fe,Mn,M)₃Si₂ phase, where M is any high melting point element or a combination of such elements. It has been shown that the area fraction of the above-mentioned phase increases with increasing content of high melting point elements. A greater area fraction of the Al₁₅(Fe,Mn,M)₃Si₂ phase in the casting from the shell mold in relation to the high pressure die casting has been also found.

The effect of austempering parameters on impact energy of non-alloyed cast iron with nodular graphite was examined. The analysis involved the austenitizing temperature in the range of 830-950 °C for the time of 60 min and the austempering temperature in the range of 300-400 °C for the time of 8, 16, 32 and 64 min. The value of the impact energy KV was determined. It has been found that the austenitizing temperature and austempering conditions affect the impact energy of ADI. The conducted analytical studies helped to develop a method for the selection of heat treatment parameters to obtain the ausferritic ductile iron with appropriate impact energy KV. The applied machine learning methods included support vector machine, decision trees, artificial neural networks and the method of variational component analysis.