Modeling the composite hardness of multilayer coated systems

The change in the composite hardness with penetration depth derived from nanoindentation tests conducted on coated systems, which involve the deposition of multilayer coatings, in general exhibits a complex shape, as a consequence of the sequential contribution of each coating layer to the composite hardness during indentation loading. In spite that there are a number of models, which have been proposed for describing the change of the composite hardness with penetration depth for monolayer coatings, as well as for determining the coating and substrate hardness, very few research works have addressed the problem of describing this kind of data for multilayer coatings. In the present communication, a rational approach is proposed for extending two models widely used for the analysis of monolayer coatings, in order to describe the composite hardness data of multilayer coatings, as well as for determining the hardness of each individual layer and that of the substrate. Thus, a modified form of the models earlier advanced by Korsunsky et al. and Puchi-Cabrera, as well as their computational instrumentation, are proposed. The extension of both models to deal with multilayer coatings is conducted on the basis of the model developed by Iost et al., in order to adapt the Jönsson-Hogmark model to the analysis of indentation data of multilayer coatings. Such a methodology provides a means of computing the volume fraction of each individual layer in the coating, which contributes to the composite hardness. According to the results obtained, this scheme seems to be general enough to be applicable to different hardness models other than the Jönsson-Hogmark model. The proposed modified models are validated employing nanoindentation results obtained from a 2024-T6 aluminum alloy coated with a diamond-like carbon film, employing electroless NiP as intermediate layer. The advantages and disadvantages of the different models employed in the analysis are thoroughly discussed.