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Predictive modeling schemes for wear in tribometers

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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. Further 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.

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