In this study, a feed-forward back-propagation artificial neural network (ANN) was designed for the predicting of the micro-hardness of nano-sized Cu-Cr solid solution. The network with 10 and 18 neurons in the first and second hidden layers, respectively, was created based on the 36 extracted data (7 variables and 36 datasets) from a similar study. Then, the genetic algorithm of the effective parameters of mechanical alloying was developed to specify the maximum hardness of Cu-Cr alloys. Consequently, the milling process was performed to validate the created network according to the obtained factors in the genetic algorithm. For this purpose, mechanical alloying was carried out by ball milling of the Cu-Cr powders at 3 and 4.5 weight percentages, respectively. The ball-to-powder weight ratio was kept at 15:1 in the Ar atmosphere and the milling times for Cu-3wt%Cr and Cu-4.5wt%Cr were 48 and 63 h, respectively. Produced powders were studied by scanning electron microscope (SEM) and X-ray diffraction (XRD). Lattice strain, crystallite size, and internal strain were calculated by Rietveld refinement, using the Maud software. Next, the produced powders compressed via a cold press and annealed at 601 °C. The micro-hardness for the Cu-3wt%Cr and Cu-4.5wt%Cr were 228 and 255 Vickers, respectively, while the predicted micro-hardness by the artificial neural network and genetic algorithm were 237 and 243 Vickers, respectively. The root mean square error was 4.25% in the regression of 0.99063 at the proposed sintering temperature. Finally, the result of the sensitivity analysis shows that the milling time, percentage of Cr, and annealing temperature had the highest impact on the micro-hardness of the products.

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In this paper, Cu-Ti nanocomposite synthesized via ball milling of copper-titanium powders in 1, 3, and 6 of weight percentage compounds. The vial speed was 350 rpm and ball to powder weight ratio kept at 15:1 under Argon atmosphere, and the time of milling was 90 h. Obtained powders were studied by scanning electron microscopy (SEM), X-ray diffraction (XRD), and dynamic light scattering (DLS). Crystallite size, lattice strain, and lattice constant were calculated by Rietveld refinement with Maud software. The results show a decrease in the crystallite size, and an increase in the internal strain and lattice parameter. Furthermore, the lattice parameter grew by increasing the percentage of titanium. Then, the powders compressed by the cold press and annealed at 650˚C. Finally, their micro-hardness and electrical resistance were measured. These analyses show that via increasing the proportion of titanium, Cu-6wt%Ti with 312 Vickers had the highest micro-hardness; due to the increasing the work hardening. Moreover, the results of the electrical resistance illustrate through increasing the amount of alloying material, the electrical resistance grew which the highest electrical conductivity was Cu-1wt%Ti with 0.36 Ω.