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The present study focuses on investigating the dominant friction and wear mechanisms in case of dry sliding of carbon nanoparticle reinforced nickel matrix composites under elastic and elasto-plastic contact conditions. For this purpose, multi-wall carbon nanotubes (CNT), onion-like carbon (OLC) and nanodiamonds (nD) were chosen to represent a large variety of carbon nanoparticles as they can be systematically distinguished regarding their carbon hybridization state (sp 2 vs. sp 3) as well as their morphology and size ("0D" vs. "1D"). Contact simulations based on the Greenwood-Williamson model are conducted in order to calculate the required contact loads. Friction and wear analysis is supported by complementary characterization techniques, including scanning electron microscopy, transmission electron microscopy, energy dispersive spectroscopy, Raman spectroscopy, light microscopy as well as laser scanning microscopy. It is found, that only CNT provide efficient lubrication as reinforcement phase in composites, presenting different lubrication mechanisms for the tested contact conditions. The high aspect ratio of CNT is found to be essential for the lubrication mechanisms, allowing the particles to be dragged into the direct tribological contact. The lubrication effect increases with increasing volume content of CNT, reaching a maximum steady state frictional reduction of 50% compared to the unreinforced nickel reference.

Despite the great importance of the real contact area, it is a parameter which, depending on the tribological system, is difficult or impossible to obtain experimentally. In this work, a combination of methods was used to estimate the development of the real contact surface, and the results were compared with the friction coefficient course. The measurements were carried out with a home-built in situ tribometer, which records a 3D image of the surface after each individual friction cycle. A tungsten sample was treated by laser interference with a line-like pattern to produce a deterministic surface. This allowed for more precise tracking of the real contact area when combined with the use of an inert corundum sphere as a counter-body. The real contact area was calculated numerically from the height information obtained using a contact application. Finally, the true contact surface was compared with the parallel-recorded friction values. After a short running-in phase, the friction behavior and the real contact surface showed comparable courses. This indicates that the changes in the real contact area could explain the friction behavior of the laser-patterned sample, and the methodology was proven to be suitable for experimentally estimating the real contact area.

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The significance of periodic surface structuring methods, such as direct laser interference patterning, is growing steadily. Thus, the ability to objectively and consistently evaluate these surfaces is increasingly important. Standard parameters such as surface roughness or the arithmetic average height are meant to quantify the deviation of a real surface from an ideally flat one. Periodically patterned surfaces, however, are an intentional deviation from that ideal. Therefore, their surface profile has to be separated into a periodic and a non-periodic part. The latter can then be analyzed using the established surface parameters and the periodic nature allows a quantification of structure homogeneity, e.g. based on Gini coefficient. This work presents a new combination of established methods to reliably and objectively evaluate periodic surface quality. For this purpose, the periodicity of a given surface is extracted by Fourier analysis, and its homogeneity with respect to a particular property is determined for the repeating element via a Gini analysis. The proposed method provides an objective and reliable instrument for evaluating the surface quality for the selected attribute regardless of the user. Additionally, this technique can potentially be used to both identify a suitable surface structuring technique and determine the optimal process parameters.

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