BP neural network analysis of dry sliding friction and wear of UHMWPE composites

Ultra high molecular weight polyethylene(UHMWPE) and its composites can be used for the sliding contact parts and soil-working components of agricultural engineering equipment since their excellent self-lubrication and anti-adhesion. Dry sliding friction and wear of UHMWPE composites filled with wollastonite fibers were simulated and estimated based on BP neural network. The BP neural network with a 3×10×2 structure was designed, trained and checked. It is shown that the designed neural network can estimate the effects of the coupling treatment method of wollastonite fibers, fiber content in the composites and the normal load during tests on the dry sliding friction and wear properties of the composites effectively. Mostly, the estimated values of the friction coefficients and worn volumes were close to the corresponding experimental data and the errors between the estimated values and the corresponding experimental data were less than 10%.