Digital Image Correlation – DIC/DVC
It is a state-of-the-art, non-intrusive optical tool for shape, strain and deformation analysis of solid, granular and liquid subjects.
LaVision was founded in 1989 as a spin-off from Max Planck Institute and Laser Laboratory in Goettingen, Germany.
LaVision offers standard and dedicated customer designed Laser Imaging Systems for reactive and non-reactive flow field analysis and fluid mechanics applications, Intelligent Imaging Systems for non-destructive material testing, a range of high-performance cameras (CCDs and intensified CCDs) and smart optical sensor systems.
The LaVision team has extensive professional experience in Laser Imaging Spectroscopy and optical techniques such as Laser Induced Fluorescence (LIF), Absorption and Emission Spectroscopy, Raman, Rayleigh and Mie Scattering, Particle Image Velocimetry (PIV), Spray Analysis, Digital Image Correlation (DIC) techniques as well as ultra-fast time-resolved imaging and high-speed image recording.
Digital Image Correlation (DIC)
Digital Image Correlation (DIC) effectively tracks the movement of the naturally occurring, or applied surface pattern during the test or experiment. This is done by analysing the displacement of the patterns within discretised subsets or facet elements of the whole image. The maximum correlation in each window corresponds to the displacement, and this gives the vector length and direction for each window. Advanced algorithms use multi-pass processing, window deformation, and the possibility of non-square subsets to maximise the sub-pixel accuracy.
The user acquires a series of images during a material testing experiment, with the first image normally being the case of zero applied load. With standard single camera or stereoscopic multi camera setups, 2D in-plane deformation or full 3D surface measurements are achieved. Local derivative calculations give the strain tensors across the entire surface, and a standard feature of StrainMaster is the ability to place a virtual strain gauge anywhere on the sample surface after the test, giving incredibly accurate strain data.
StrainMaster from LaVision offers a DIC system that is appropriate for many applications; small or large scale, and long term or ultra-fast dynamic tests. We also offer a special volume correlation upgrade for the analysis of tomographic images for full 3D analysis of internal material structures.Click here for more information
Digital Volume Correlation (DVC)
Digital Volume Correlation (DVC) is a novel technique for full 3D strain and deformation measurements. The technique imports volume images of the component in reference and deformed states and is able to calculate the full 3D displacement and strain map. Images are typically acquired from X-ray Computed Tomography (X-ray CT) systems, but can equally be obtained by Magnetic Resonance Imaging (MRI) systems for biological subjects, or via optical tomography for transparent media; for which LaVision offer our patented Tomographic reconstruction algorithms as an add-on. DVC is a powerful non-intrusive technique for the identification of sub-surface material deformation and is capable of identifying defects, discontinuities or other material characteristics.
Like its counterpart, Digital Image Correlation (DIC) which is restricted to surface-only measurements, in order for Digital Volume Correlation to be successful the volume images must contain a random pattern. That random pattern is seen as changes in local contrast. In the case of X-ray CT scans the pattern will be produced by changes in material density such as air voids in concrete, or particles of different material type within the main body matrix, such as tin particles distributed in an aluminium powder. The volume image is sub-divided into interrogation volumes within which robust and highly accurate algorithms calculate the displacement of the pattern, which represent the material shift. Digital Volume Correlation is capable of yielding over one million displacement vectors per volume image pair.Click here for more information