A predictive modeling scheme for wear in pin-on-disc and twin-disc tribometers

Abstract

Study of wear in complex micro-mechanical components is often accomplished experimentally using a pin-on-disc and twin-disc tribometer. The present paper proposes an approach that involves a computationally efficient incremental implementation of Archard's wear model on the global scale for modeling sliding and slipping wear in such experiments. It will be shown that this fast simplistic numerical tool can be used to identify the wear coefficient from pin-on-disc experimental data and also predict the wear depths within a limited range of parameter variation. Furthermore, it will also be used to study the effect of introducing friction coefficient into the wear model and also to model water lubricated experiments. A similar tool is presented to model wear due to a defined slip in a twin-disc tribometer. The resulting wear depths from this tool is verified using experimental data and two different finite element based numerical tools namely, the Wear-Processor, which is a FE post-processor, and a user-defined subroutine UMESHMOTION in the commercial FE package ABAQUS. It will be shown that the latter two tools have the potential for use in predicting wear and the effective life span of any general tribosystem using the identified wear coefficient from relevant tribometry data.