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Wear modelling of diamond-like carbon coatings against steel in deionised water

Diamond-Like Carbon (DLC) coatings are thin protective surface coatings used to reduce friction and minimise wear in a wide range of applications. The focus of this work is the use of DLC coatings within Rolls-Royce’s pressurised water reactors. A strong understanding of material behaviour in this environment is compulsory due to the stringent safety requirements of the nuclear industry. Wear testing of a range of commercial DLC coatings against steel in water, and the dependence of the tribology on normal load, sliding distance, and environmental species, was examined. Wear depth was observed to increase with normal load, and increase non-linearly with sliding distance. Uniquely, it was suggested that the tribology of a DLC coating in water was controlled by the velocity accommodation mode (VAM) of the transfer layer. When interfacial sliding was the dominant VAM, the carbonaceous transfer layer was present at all times, and a low specific wear rate was observed. When shear and recirculation of debris was the dominant VAM, the carbonaceous transfer layer initially present was replaced by iron oxide species, and a high specific wear rate was observed as a result of a three-body mechanism involving hematite. Two individual wear models were developed to predict the wear depth of a DLC coating sliding against steel in water. Each model represents a novel extension to the current literature regarding the modelling of wear. Firstly, an analytical differential equation was derived to predict the wear depth of a ball and a flat surface, in relation to any phenomenological law for wear volume. Secondly, a unique formulation of an incremental wear model for an arbitrary geometry was developed for a DLC coating which included the growth of a transfer layer. An efficient methodology was presented to allow fast integration of the equations whilst damping numerical instabilities. A comparison between the analytic and computational wear models showed a strong agreement in the model predictions, with a comparative error of less than 5%.

Identiferoai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:628705
Date January 2014
CreatorsSutton, Daniel Christopher
ContributorsWood, Robert
PublisherUniversity of Southampton
Source SetsEthos UK
Detected LanguageEnglish
TypeElectronic Thesis or Dissertation
Sourcehttps://eprints.soton.ac.uk/366526/

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