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A tribological system is a complex non-linear system composed of the elements that are connected structurally and functionally. The aim of this paper is to present an overview of artificial neural networks, its development and applications of neural networks in the prediction of tribological properties of dental glass ceramic using a newly measured ball-on-plate nanotribometer. The possibility of artificial neural networks application to solve complex nonlinear problems and to identify tribological characteristics of dental glass ceramic in terms of wear rate and coefficient of friction are presented in this paper.

The structural, mechanical and tribological properties of ZA-27/SiC nanocomposites were investigated at micro/nanoscale. The nanocomposites with different volume fractions of nano-sized SiC particles were produced using the compocasting technique. The microstructure of nanocomposites was characterized with formation of SiC nano agglomerates, which were relatively uniformly distributed. The increase in SiC content contributed to the uniformity of their distribution. Also, the phenomenon of particle segregation in the form of particle-rich clusters, as well as particle-porosity clusters, was identified. The density level of composites decreased with the increase of the SiC content. The porosity followed a reverse trend. The tendency for formation of local particle-porosity clusters was the highest in ZA-27/1% SiC nanocomposite, causing the highest level of porosity. Increasing percentage of SiC content was followed by the increase in micro/nanohardness of the composites. The results of micro/nanoscale tribotests revealed that the reinforcing with SiC nanoparticles significantly improved wear and friction behavior of ZA-27 matrix alloy. The rate of improvement increased with the increase of SiC nanoparticle content, load, and sliding speed. The highest degree of changes corresponded to the change of the SiC nanoparticle content from 0 to 1 wt%. The further decrease of wear with SiC content (from 1 to 5 wt%) was almost linear. The different tribological behavior of tested ZA-27 matrix and ZA-27/SiC nanocomposites was influenced by differences of intensity of adhesion resulted in transferred layers of matrix material onto worn surfaces of Al2O3 ball counterpart. The intensity of adhesion significantly decreased with the increase of SiC nanoparticle content.

Mechanical and tribological investigation of obtained nanocomposites is presented in this paper.As matrix material, well known tribological zinc-aluminium alloy, ZA-27 was used. Nanocomposites were obtained by compocasting procedure, while as reinforcement Al 2 O 3 nanoparticles with average size of 20-30 nm was used. Nanocomposites with three different volume fractions were obtained. In order to get insight in structure and mechanical properties of obtained nanocomposites density and hardness measurements were performed. Tribological properties of tested materials were investigated using block-on-disc tribometer in dry sliding conditions with variation three different values of sliding speed and normal load. Wear tracks that were generated as a result of dry sliding process were analysed using optical and scanning electron with EDS microscope.

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