Digital image correlation

About: Digital image correlation is a research topic. Over the lifetime, 7842 publications have been published within this topic receiving 132166 citations.

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

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.

Measurement of ground displacement from optical satellite image correlation using the free open-source software MicMac

Abstract: Image correlation is one of the most efficient techniques to determine horizontal ground displacements due to earthquakes, landslides, ice flows or sand dune migrations. Analyzing these deformations allows a better understanding of the causes and mechanisms of the events. By using sub-pixel correlation on before- and after-event ortho-images obtained from high resolution satellite images it is possible to compute the displacement field with high planimetric resolution. In this paper, we focus on measuring the ground displacements due to seismotectonic events. The three sub-pixel correlators used are: COSI-Corr – developed by Caltech, a free, closed-source correlator, dependent on commercial software (ENVI) and widely used by the geoscience community for measuring ground displacement; Medicis – developed by CNES, also a closed-source correlator capable of measuring this type of deformation; and MicMac – developed by IGN, the free open-source correlator we study and tune for measuring fine ground displacements. We measured horizontal ground deformation using these three correlators on SPOT images in three study cases: the 2001 Kokoxili earthquake, the 2005 dyke intrusion in the Afar depression and the 2008 Yutian earthquake.

Identification of Mechanical Material Behavior Through Inverse Modeling and DIC

Abstract: Inverse methods offer a powerful tool for the identification of the elasto-plastic material parameters. One of the advantages with respect to classical material testing is the fact that those inverse methods are able to deal with heterogeneous deformation fields. The basic principle of the inverse method that is presented in this paper, is the comparison between experimentally measured strain fields and those computed by the finite element (FE) method. The unknown material parameters in the FE model are iteratively tuned so as to match the experimentally measured and the numerically computed strain fields as closely as possible. This paper describes the application of an inverse method for the identification of the hardening behavior and the yield locus of DC06 steel, based on a biaxial tensile test on a perforated cruciform specimen. The hardening behavior is described by a Swift type hardening law and the yield locus is modeled with a Hill 1948 yield surface.