Characterization, pore size measurement and wear model of a sintered Cu–W nano composite using radial basis functional neural network

• Wear tests were conducted on Cu–(5–20%) W nano composite.
• SEM, TEM, XRD and EDS were used to evaluate the characteristics of composite.
• Wear mechanism was discussed through the distribution map and SEM.
• Best trained RBFNN was developed to predict the unknown values.

Cu–(5–20%) W composite preforms, with a density of 94% were prepared through mechanical milling, mixing, compaction, sintering and hot extrusion. The X-ray Diffraction analysis, Particle Size analysis, Transmission Electron Microscope, Scanning Electron Microscope and Energy Dispersive Spectrum were used for the characterization studies. The pore size during different sintering atmospheres and the pore size reduction during extrusion, were studied through Auto CAD 2010 software. The wear experiments were conducted using the pin-on-disc wear tester. The various regions in the wear mechanisms were identified through the wear distribution map. The Radial Basis Functional Neural Network has been used in an attempt to predict the mechanical and tribological behavior of composites, and useful conclusions have been made.

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