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Lecture

How normal load influences the microstructure evolution in copper under reciprocating tribological loading

Thursday (27.09.2018)
12:15 - 12:30 S1/01 - A5
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Tribology focuses on friction, wear and lubrication of interacting surfaces in relative motion. Near the interface of two solid bodies, the microstructure changes due to the sliding contact. The development and characteristics of the modified material strongly influence the tribological behavior. By controlling the microstructure, tribological properties like friction coefficient and wear rate can be tailored. The elementary mechanisms for these microstructural changes are not fully understood. Consequently, we are concentrated on researching the influence of the normal load and sphere diameter on the microstructure evolution after a single sliding pass. Dry sliding tests were performed on oxygen free high conductivity copper plates. A sapphire sphere was used as a counterbody. A normal load of 1 to 100 N was achieved by varying the Hertzian contact pressure (530 to 1953 MPa) and sphere diameter (1 to 10 mm). Scanning electron, focused ion beam and transmission electron microscopy were used to probe the subsurface deformation. In all experiments, a sharp line-like feature, called dislocation trace line (DTL), was identified at a uniform mean depth of about 90…390 nm beneath the sample’s surface. The higher the normal load, the deeper is the DTL. Transmission electron microscopy results reveal that in case there are multiple DTLs, only one has a unique tilting characteristic. The sphere diameter and hence the stress field beneath the indenter strongly influence the microstructure evolution. At 100 N normal load, subgrains are formed between the sample’s surface and the DTL. One aim of this study is the formulation of a model description for microstructural changes in order to predict and tailor tribological properties.

Speaker:
Friederike Ruebeling
Karlsruhe Institute of Technology (KIT)
Additional Authors:
  • Dr. Gunther Richter
    Max Planck Institute for Intelligent Systems
  • Prof. Dr. Peter Gumbsch
    Karlsruhe Institute of Technology (KIT)
  • Dr. Christian Greiner
    Karlsruhe Institute of Technology (KIT)