Identifying the material properties of human skin
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Identifying the material
properties of human skin
Sam EvansSchool of Engineering, Cardiff
University
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Introduction
•Many engineering applications for a model of skin
•Detailed, layered, 3D models for localised effects (eg needles, shaving)
•Membrane model more efficient for large areas of skin
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Property identification
•Need to identify properties for use in a constitutive model
•Quick, non - invasive measurements needed because of wide variations in properties
•Can use a model to measure the properties as well as to predict deformation
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FE model•2D plane stress model with
unaligned mesh• Incompressible Ogden material
model, with tension field wrinkling & prestretch1
•Hager – Zhang nonlinear conjugate gradient solver
1. Evans, S. L., On the implementation of a wrinkling, hyperelastic membrane model for skin and other materials. CMBBE, in press.
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Computational measurements
“Consider a nonlinear material with large strains... The task of interpreting the experimental tensile test then becomes nontrivial... Probably this task will eventually be taken over by computer- aided experiment, 'back- calculating' by trial and error using finite elements...”
- Bruce Irons, 1980
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Digital image correlation
•Subsets matched in other camera view and later images
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X displacement (m)
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X displacement (m)
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Y displacement (m)
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X error (m)
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Y error (m)
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Optimisation•Hager – Zhang solver and simple
model gives reliable solution <0.5s
•Many iterations possible
•Simplex optimisation (Matlab fminsearch)
•Stochastic optimisation (Alexandre Delalleau, Pierre Fabre)
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RMS error (m)
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p
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p
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Improved stochastic algorithm
•Random points within bounds
•Fit regression plane through best points
•Randomly generate new points around regression plane
•Effectively changes the axes for the bounding box to suit the problem
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p
Low load
All loadsMedium load
High load
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High load
Medium load
Low load
All loads
p
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Discussion•The skin stiffens dramatically at
low loads but not so much at high loads
•A single Ogden model doesn't fit all loads perfectly
• Identification based on a single frame is as effective as using multiple frames
•Some uncertainty remains
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Conclusions•Hager – Zhang algorithm is
excellent for highly nonlinear models
•Simplex optimisation can be misleading
•Modified stochastic method works well but there is some inherent uncertainty when identifying three parameters
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