Cast stainless steel of the Cr-Ni duplex type is used, among others, for the cast parts of pumps and valves handling various chemically

aggressive media. Therefore, the main problem discussed in this article is the problem of abrasion wear resistance in a mixture of SiC and

water and resistance to electrochemical corrosion in a 3% NaCl- H2O solution of selected cast steel grades, i.e. typical duplex cast steel,

high silicon and manganese duplex cast steel, and Cr-Ni austenitic cast steel (type AISI 316L). The study shows that the best abrasion

wear resistance comparable to Ni-Hart cast iron was obtained in the cast duplex steel, where Ni was partially replaced with Mn and N.

This cast steel was also characterized by the highest hardness and matrix microhardness among all the tested cast steel grades. The best

resistance to electrochemical corrosion in 3% NaCl- H2O solution showed the cast duplex steel with high content of Cr, Mo and N. The

addition of Ni plays rather insignificant role in the improvement of corrosion resistance of the materials tested.

Recently, the need to develop fuel efficient transport systems has led to the development of a range of materials of low density, high stiffness and high strength each can be made at a reasonable cost. The aluminium based alloys are particularly important because of their improved mechanical, physical and technical properties. Fatigue failures have been recognised since the early days of the industrial revolution. Fatigue response of most of materials is related with the microstructural variations in the structure. Hence, in this study, influence of particle size and volume fractions on fatigue properties of Al-alloy composites was investigated. It was found that particle size and volume fraction of reinforcement particles play significant role on fatigue propagation rates, stress intensity threshold values, crack tip opening distance and crack tip plastic zone sizes.

The paper presents a methodology of modeling relationships between chemical composition and hardenability of structural alloy steels using computational intelligence methods, that are artificial neural network and multiple regression models. Particularly, the researchers used unidirectional multilayer teaching method based on the error backpropagation algorithm and a quasi-newton methods. Based on previously known methodologies, it was found that there is no universal method of modeling hardenability, and it was also noted that there are errors related to the calculation of the curve. The study was performed on large set of experimental data containing required information on about the chemical compositions and corresponding Jominy hardenability curves for over 400 data steel heats with variety of chemical compositions. It is demonstrated that the full practical usefulness of the developed models in the selection of materials for particular applications with intended performance in the area of application.