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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. [ABSTRACT FROM AUTHOR]

Highlights: [•] Wear tests were conducted on Cu & Cu–W sintered and extruded composite. [•] Analyzed wear rate and coefficient of friction with respect to sliding distance. [•] SEM observations were analyzed to evaluate the worn surface and debris particles. [•] Design expert software was used to develop the map and model. [Copyright &y& Elsevier]

Abstract: High-energy mechanical milling was used to mix Cu and W powders. Cylindrical preforms with initial preform density of 85% were prepared using a die and punch assembly. The preforms were sintered in an electric muffle furnace at 750°C, 800°C, 850°C, and subsequently furnace cooled and then the specimens are hot extruded to get 92% preform density. Scanning Electron Microscope and X-ray diffraction observations used to evaluate the characteristics. The pore size reduction during extrusion was studied using Auto CAD 2010. Neural networks are employed to study the tribological behavior of sintered Cu–W composites. The proposed neural network model has used the measured parameters namely the weight percentage of tungsten, sintering temperature, load and sliding distance to predict multiple material characteristics, hardness, specific wear rate, and coefficient of friction. The predicted values from the proposed networks coincide with the experimental values. In addition, a relative study between the regression analysis and the networks revealed that the artificial neural networks can predict the tribological characteristics of sintered Cu and W composites better than regression polynomials within a very few percent error. [Copyright &y& Elsevier]

Highlights: [•] Wear tests were conducted on Cu–W nano composite. [•] SEM and EDS observations were tried to evaluate the wear characteristics. [•] RSM was used to develop the numerical model. [•] Wear mechanism map was developed to identify the various regions. [ABSTRACT FROM AUTHOR]