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May 20-21, 2019 | Vienna, Austria
Biomaterials and Nanomaterials &
Materials Physics and Materials Science
2nd International Conference on
Journal of Materials Science and Nanotechnology | Volume 3
Influencing parameters and temperature impact on the fatigue crack growth
behaviour of rubbers
Bernd Schrittesser
Polymer Competence Center Leoben GmbH, Austria
The unique mechanical properties of rubbers make
them suitable for applications in which cyclic loadings
are involved. In this loading condition, failure is mainly
related to fatigue and therefore the understanding of
the phenomena connected to it results fundamental for
a reliable lifetime prediction of rubber products. In the
field of elastomeric materials, one of the main approaches
followed for fatigue life prediction is the crack growth
approach. This is based on the study of the growth of
pre-existing cracks up to end of service life using tearing
energy as fracture mechanical parameter. The fatigue
behaviour of rubbers is influenced by a large number
of parameters, which can be related to the mechanical
history, environmental conditions and rubber formulation.
In order to investigate more into details how the fatigue
crack growth behaviour is influenced by the different
involved parameters, pure shear specimens were loaded
cyclically at different loading conditions and mechanical
histories. A camera system was implemented for crack
growth detection and the surface temperature was
recorded using an IR sensor. A detailed investigation of
the influence of different parameters was hence carried
out. In particular, the influence of waveform, load and
displacement control, mechanical history, frequency and
temperature were studied in detail. Moreover, the heat
build-up during cyclic loading was further investigated, by
monitoring the surface temperature through an IR camera.
The aim of this research is to provide a further description
of fatigue crack growth in rubbers by defining the influence
of different parameters involved during cyclic loading.
From a deeper understanding of these influences, models
that can supply more accurate lifetime predictions could
be developed.
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