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The comparative analysis of the two hybrid composites, obtained by the compocasting process, based on A356 and ZA-27 alloys and reinforced with 10 wt. % SiC and 0, 1 and 3 wt. % graphite (Gr) is presented. Optimisation of their tribological behaviour was performed by the Taguchi method and the artificial neural network (ANN). Tribological tests were performed on a block-on-disk tribometer, at three sliding speeds, three normal loads and a sliding distance of 600 m, without lubrication. Results obtained by the ANOVA showed that the hybrid composites based on A356 alloy have the better wear resistance and that the composite reinforced with 10 wt. % SiC and 1 wt. % Gr has the lowest wear rate. The lowest coefficient of friction was obtained for a composite based on ZA-27 alloy reinforced with 10 wt. % SiC, at a normal load of 10 N and sliding speed of 1 m/s.

This paper provides a brief overview on the mechanical and tribological properties of aluminiumbased composites reinforced with various nanoparticles. The amount of reinforcement was, most often, up to 5 % (vol. or wt.). Based on the reviewed results, it can be observed that by increasing the amount of reinforcement, properties such as hardness and density increase significantly. Tribological tests were performed mostly on pin-on-disc tribometers under dry sliding conditions. Composites reinforced with ceramic nanoparticles such as Al2O3, SiC, TiC, TiO2 and TiB2 showed a higher wear resistance than their matrix alloys. The microstructure of the nanocomposites and morphology of the worn surfaces were in most cases studied by scanning electron microscopy (SEM), transmission electron microscopy (TEM) and energy dispersive spectroscopy (EDS).

This paper presents the results of experimental research of efficiency of a worm gearbox which was designed for that purpose. The gearbox housing is of a welded construction with side openings, which enables simple assembly and disassembly of worm pairs, bearings and other elements. Two worm pairs with the same geometric characteristics were used for testing, where the worms are made of improved steel 42CrMo4 and the worm gears are made of zinc-aluminum alloy ZA12 and aluminum alloy A356. The values of the efficiency were determined at different operating modes of the gearbox (loads and rotational speeds), where mineral oil with a viscosity of 460 mm2/s was used as a lubricant. According to the previously defined working conditions, the power losses and friction coefficient of the worm gear pairs were calculated. Based on the test results, it is concluded that the values of the efficiency of the worm gear pair 42CrMo4/ZA12 are significantly higher compared to the worm gear pair 42CrMo4/A356, which is the result of lower power losses and lower values of the coefficient of friction.

The zinc-aluminium casting alloy ZA-27 is well-established and is a frequently used material for plain bearing sleeves. It has good physical, mechanical and tribological properties. Its tribological properties can be improved further by adding hard ceramic particles to the alloy. The tested nanocomposites were produced by the compocasting process with mechanical alloying preprocessing (ball milling). Three different amounts of SiC nanoparticles, with the same average size of 50 nm, were used as reinforcement, i.e. 0.2, 0.3 and 0.5 wt. %. Tests were performed on a block-on-disc tribometer (line contact) under lubricated sliding conditions, at two sliding speeds (0.25 and 1 m/s), two normal loads (40 and 100 N) and a sliding distance of 1000 m. The prediction of wear rate was performed with the use of an artificial neural network (ANN). After training the ANN with architecture 3-4-1, the regression coefficient for the network was 0.99973. The experimental values and values obtained by applying the Taguchi method were compared with the predicted values, showing that ANN is more efficient in predicting wear.

The aim of this study was optimization of wear of nanocomposites with A356 alloy as a matrix. Different percentages of Al2O3 particles (0.2, 0.3 and 0.5 wt. %), with two average size (25 and 100 nm), were dispersed in the A356 matrix and fabricated by compocasting process. The tribological tests were conducted by the orthogonal L24 matrix. By application of the RSM method, a mathematical model was developed that best describes wear dependence on the observed factors and it was found that all considered factors have a significant impact on the wear of nanocomposite.

Optimization of tribological behaviour of hybrid composites based on A356 and ZA27 alloys reinforced with 10 wt. % SiC and 0, 1 and 3 wt. % Gr is presented in this paper. Optimization was performed using the Taguchi method and the artificial neural network (ANN). Hybrid composites were obtained by compocasting procedure. The analysis shows that hybrid composites based on A356 alloy have better tribological characteristics, while the one reinforced with 10 wt. % SiC and 1 wt. % Gr shows the least wear.

Surface texturing is one of the surface modification techniques which deliberately change the texture of the surface, in order to improve, among other things, its tribological performance. This is obtained through different patterns, which can be on micro or nano scale, created on the contact surfaces. The performance of a textured surface depends on the shape, geometry and pattern of the surface texture and the operating condition of the components in contact. There is a number of various techniques for surface texturing, among which laser surface texturing is most often used. The different surface texture shapes, different textured area ratios and patterns, different lubrication regimes with different contact geometries and materials have been subject of theoretical and experimental research for many years. This paper reviews the state-of-the-art of researches that consider various surface texturing for tribological application, as well as its effect on performance enhancement. Conclusions of this paper may provide guidance for optimal design of surface textures in practical engineering applications.

This paper analyses wear behaviour of Al-Si alloy A356 (AlSi7Mg) based composite reinforced with 10 wt. % SiC, and compare it with the base A356 alloy. Composite are obtained using the compocasting procedure. Tribological testing have been conducted on a block-on-disc tribometer with three varying loads (10, 20 and 30 N) and three sliding speeds (0.25, 0.5 and 1 m/s), under dry sliding conditions. Sliding distance of 300 m was constant. The goal of the paper was to optimize the influencing parameters in order to minimize specific wear rate using the Taguchi method. The analysis showed that the sliding speed has the greatest influence on specific wear rate (39.5 %), followed by the load (23.6 %), and the interaction between sliding speed and load (19.4 %). A regression analysis and experiment corroboration was conducted in order to verify the results of the optimization. Specific wear rate prediction was done using artificial neural network (ANN).