The coefficient of friction (CoF) obtained from tribological tests conducted on textured and porous surfaces was analysed using the machine learning Radial Basis Function (RBF) method. Non-textured and non-porous samples were taken as reference surfaces. Test parameters, such as entrainment velocity and slide-roll ratio (SRR), along with geometric characteristics of surface features (e.g. texture width and depth, coverage area, circularity, spatial distribution and directionality, among others), were selected as training dataset for the machine learning RBF model. The surface features were divided into designed patterns (dimples and grooves) manufactured by laser texturing, and randomised cavities (surface pores) resulted from the sintering process. The principal outcomes of this study are the effective use of the machine learning RBF method for tribological applications, as well as a critical discussion on its feasibility for the experimental dataset selected and the preliminary results obtained. Main results show that the Hardy multiquadric radial basis function provided an overall correlation coefficient of 0.934 for 35 poles. The application of the suggested machine learning technique and methodology can be extended to other experimental results available in the literature to train more robust models for predicting tribological performances of textured and structured surfaces.

Aluminum bronze is a complex group of copper-based alloys that may include up to 14% aluminum, but lower amounts of nickel and iron are also added, as they differently affect alloy characteristics such as strength, ductility, and corrosion resistance. The phase transformations of nickel aluminum–bronze alloys have been the subject of many studies due to the formations of intermetallics promoted by slow cooling. In the present investigation, quaternary systems of aluminum bronze alloys, specifically Cu–10wt%Al–5wt%Ni–5wt%Fe (hypoeutectoid bronze) and Cu–14wt%Al–5wt%Ni–5wi%Fe (hypereutectoid bronze), were directionally solidified upward under transient heat flow conditions. The experimental parameters measured included solidification thermal parameters such as the tip growth rate (VL) and cooling rate (TR), optical microscopy, scanning electron microscopy (SEM) analysis, hardness, and microhardness. We observed that the hardness and microhardness values vary according to the thermal parameters and solidification. We also observed that the Cu–14wt%Al–5wt%Ni–5wi%Fe alloy presented higher hardness values and a more refined structure than the Cu–10wt%Al–5wt%Ni–5wt%Fe alloy. SEM analysis proved the presence of specific intermetallics for each alloy.

The Cu-8.5wt % Sn alloy presents an extensive microsegregation during its solidification. That microsegregation results in the formation of a eutectoid mixture, which is detrimental to subsequent forming processes. This study deals with the influence of solidification time and cooling rate on the microstructure of that alloy. The unidirectional solidification technique allowed the acquisition of thermal data. The optical microscopy enabled the microstructural characterization of the material, the measurement of dendrite arm spacings and the quantification of the volume fraction of the eutectoid mixture. A semi-analytical mathematical model was proposed to estimate the volume fraction of the eutectoid mixture. The model expresses the volume fraction as an implicit function of the Fourier number. The results showed that the microstructure is dendritic and that the characteristic spacings increase with the solidification time between the liquidus and the peritectic temperatures. The data also showed that for higher cooling rates the dendrite arm spacings are smaller and that there is a tendency for the volume fraction of eutectoid mixture in the columnar zone to increase with the Fourier number and to decrease with the cooling rate. The proposed model allowed obtaining values of volume fraction with the same order of magnitude of the experimental data, but with behavior tendency opposite to that observed.

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