Abstract
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.
Highlights
Ductile iron continues being one of the most dynamically developing cast materials in the world
Studies to improve the properties of ductile iron have led to the development of materials such as austempered ductile iron (ADI) or ausferritic nodular cast iron with carbides (NCIC)
The results reviewed in this article show beneficial effect of alloying elements on the microstructure of ductile iron, which means the possibility of obtaining either bainitic or ausferritic matrix with carbides increasing the wear resistance [8,9,10,11,12,13,14]
Summary
Ductile iron continues being one of the most dynamically developing cast materials in the world. A tool for the efficient and automatic classification of materials will serve as an aid supporting decisions regarding the selection of construction materials In this way, the technologist will be able to make the best choice from among the wide range of various, but often similar, materials that meet the most demanding technological criteria, such as the tensile strength (Rm), the force needed to break the material sample, and yield strength (Rp0.2), the stress a material can withstand without permanent deformation, elongation (A), and hardness (HB). This method can be improved by a preliminary analysis of clusters based on properties, making classification more rough but at the same time flawless (Fig. 1)
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