PREDICTION OF HARDNESS AND ELECTRICAL PROPERTIES IN ZRB(2) PARTICLE REINFORCED METAL MATRIX COMPOSITES

In the present study, the hardness and electrical properties of copper based composite prepared by hot pressing of mechanically alloyed powders were predicted using Artificial Neural Network (ANN) approach. Milling time (t, h), particles size of mechanically alloyed powders (d, nm), dislocation density (rho, m(-2)) and compressive yield stress (sigma(0.2), MPa) were used as inputs. The ANN model was developed using general regression neural network (GRNN) architecture. Cu-based composites reinforced with micro and nano ZrB2 particles were consolidated via powder metallurgy processing by combining mechanical alloying and hot pressing. Analysis of the obtained results concerning hardness and electrical properties of the Cu-7 vol.% ZrB2 alloy showed that the distribution of micro and nano ZrB2 particles and the presence of agglomerates in the Cu matrix directly depend on the milling time. Also, the results show a strong influence of the milling time on hardness and electrical properties of Cu-7 vol.% ZrB2 alloy. Addition of ZrB2 particles decreases electrical conductivity of copper, but despite this fact Cu-7 vol.% ZrB2 alloy can be marked as highly conductive alloy (samples made of mechanically alloyed powders milled longer than 20 h). Experimental results of the samples have shown a consistency with the predicted results of ANN.