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Measuring the mechanical properties of human skin in vivo using digital image correlation and finite element modelling

Evans, Samuel Lewin ORCID: https://orcid.org/0000-0003-3664-2569 and Holt, Catherine Avril ORCID: https://orcid.org/0000-0002-0428-8078 2009. Measuring the mechanical properties of human skin in vivo using digital image correlation and finite element modelling. Journal of Strain Analysis for Engineering Design 44 (5) , pp. 337-345. 10.1243/03093247JSA488

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Abstract

The mechanical properties of the skin are important in many applications, but are not well understood. This paper presents a method for measuring the mechanical properties of human skin in vivo using digital image correlation, with a finite element model that was used to optimize the material properties to obtain the best match with the model data. The skin was modelled as an Ogden hyperelastic membrane, with a tension field wrinkling model and an initial stretch identified as an additional material parameter, and the boundary conditions were the measured load and the displacements around the edge of the region of interest. Fast, reliable convergence was obtained using a Hager–Zhang non-linear conjugate gradient solver. A stochastic optimization procedure was used to identify the material parameters. Good estimates of the material parameters could be obtained from the displacement field at a single time point. Typical material parameters were μ = 10 Pa, α = 26, and an initial strain of 0.2. These parameters were not unique; the stochastic optimization procedure gave good global convergence and an indication of the overall uncertainty in the identification of the results. It is argued that the use of the DIC technique, which generates very large amounts of data, also gave a clearer picture of the overall uncertainty.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Uncontrolled Keywords: mechanical properties; in vivo human skin; digital image correlation; finite element model; stochastic optimization procedure
Publisher: SAGE Publications
ISSN: 0309-3247
Last Modified: 17 Oct 2022 09:43
URI: https://orca.cardiff.ac.uk/id/eprint/5414

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