Evaluation and prediction of the effect of load frequency on the wear properties of pre-cracked nylon 66

Abstract

Abstract Nylon 66 has been widely used for numerous mechanical applications but its sliding wear mechanisms are not fully understood. In particular, limited attention has been paid to the generation of fatigue surface cracks under constant and cyclic load conditions. The present work focuses on the effect of load frequency on the wear behavior of a polymer with surface defects in dry sliding conditions. The defects were imposed vertical deep cracks perpendicular to the direction of sliding. Wear studies were conducted against a steel counterface at constant loads, and in cyclic loads at different frequencies. Artificial neural network (ANN) models were examined to identify one that optimally simulates wear under the applied load parameters. Surface cracks were found to have a remarkable adverse effect on the wear behavior of the polymer. The wear rates were influenced by the number of cracks as well as the type of applied load. Furthermore, results suggest that the presence of surface cracks is attributable to the section B wear regime. Finally, acceptable predicted wear rate values were obtained by introducing the ANN wear model.