Computational study of impact wear: Fatigue approach

Abstract

Impact wear results from the surface failure of engineering components that undergo repetitive contact motion. It reduces their service life. This study investigated the impact wear of alloy steel using computational techniques combined with empirical data. The ultimate objective was to develop an approach requiring low–moderate effort for industrial application using limited resources to predict the average impact wear rate of AISI 4140 alloy steel (one of the most commonly used materials). The wear mechanisms were investigated by scanning electron microscopy (SEM). Indirect monitoring was used to examine the wear stage transition cycle, e.g., from mild to severe wear. The empirical results showed that the transition cycle depended on the impact force. Fatigue was identified as the key damage mechanism, with cracks and lamellar wear appearing under severe deterioration. The average wear rate from the initial impact cycle to the wear stage transition cycle increased as the impact force increased. A simplified computational 2D finite element model assuming a smooth frictionless surface was used to investigate the contact condition. The contact behavior including the stress–strain distribution that developed during first contact was investigated using finite element analysis. A combined empirical and computational approach to predicting average wear rate was developed based on stress distribution data from the FE model and fatigue properties. The prediction results were then validated by comparison with the empirical data. These showed good agreement. The stress amplitude derived from an equivalent high-stress region was also introduced. The predicted curve of average wear rate versus stress amplitude of an equivalent high-stress region improved the design of the component.