Development of new scientific disciplines such as bioengineering and micro-nano engineering adopting nonconventional materials requests innovative methodologies that can accurately measure the mechanical properties of soft biological materials and characterize surface energy and adhesion properties of them, independent of measurement conditions. One of emerging methods to measure the deformation of materials under stress is digital image correlation (DIC) technique. As a noncontact strain measurement method, DIC has the advantages of prevention of experimental errors caused by the use of contact type sensors and of flexibility in its application to soft materials that are hard to be tested by conventional method. In the first part of the thesis, 2 dimensional and 3 dimensional DIC codes were developed and optimized, and then applied to two critical applications: 1) determining the stress-strain behaviour of polydimethylsiloxane (PDMS) sample, as a model soft material, using the optical images across large deformation region, and 2) detecting the stiffness variation within the gel mimicking the breast tumour using ultrasound images. The results of this study showed the capability of DIC as a strain sensor and suggested its potential as a diagnosing tool for the malignant lesion causing local stiffness variation.
In the characterization of surface energy and adhesion properties of materials, two most common methods are contact angle measurement and JKR-type indentation test. In the second part of the thesis, the experimental set-up for these methods were developed and verified by using the PDMS in static (quasi equilibrium) state. From the dynamic tests, it showed its possible usage in studying adhesion hysteresis with respect to speed. The adhesion hysteresis was observed at high speed condition in both contact angle measurement and JKR-type indentation tests.
| Identifer | oai:union.ndltd.org:WATERLOO/oai:uwspace.uwaterloo.ca:10012/6097 |
| Date | January 2011 |
| Creators | Han, Yougun |
| Source Sets | University of Waterloo Electronic Theses Repository |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
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