Let's say you have a small object and would like to know more about its shape than what a 2D image can tell you. Suitable methods for 3D imaging such samples are, e.g., microCT, nanoCT, laser profilometer, MRI, and OPT. Most of these techniques require expensive devices and might only be suitable for some sample sizes and types. Out of those mentioned above, Optical projection tomography(OPT) has been commonly used in biological and medical imaging fields. OPT is a method where the sample is rotated a full rotation between a light source and a camera, and multiple images are recorded during this rotation. A set of images is called a stack, and it is handled through various processes, and finally, an accurate 3D reconstruction is made out of the sample.
In micromechanical material characterization, it is crucial to know the accurate cross-sectional shape of the sample. In the case of synthetic fibres e.g. carbon, glass, or polymer fibres, cross-sections can be measured from a single projection. But in the case of natural fibres, single projection is not an accurate way to measure the cross-section of the sample. For example, flax fibres have constant variation in their cross-sections varying from elliptical, circular, or even almost square shapes. This variation is shown in Figure 1 with OPT reconstruction of a 10mm long section with a total of 830 million voxels. Another example is the twisting of a sample, which is typical for pulp fibres.
FIBROOpt is a device that combines conventional OPT with micro-robotics. This gives the possibility to image long samples with extreme resolutions. The device can calibrate itself up to the optical resolution of the used optics. The process is fully automated, and the user only has to choose which part of the sample is imaged. End result of the process is a voxel cloud that can be transformed into cross-sectional slices or a 3d model that can be imported into FEM software for accurate modeling of the behavior of the material.