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.
So, why would you waste your time making microtomic slices to measure your samples locally and just save your energy and just use OPT? Hit us with a call, email, fill out the form or follow us @linkedin and we will guide you toward OPT measurements.
If you were interested in the shear properties of a composite structure, you would measure them conventionally on a laminate scale. Applicable tests would be e.g., short beam shear, v-notch shear or ±45° tension shear tests. Each of these tests requires a unique sample and usually additional sensors e.g., strain gauges or digital image correlation systems. When the setup is complex, it increases the amount of time needed for testing, material usage grows, and there are added sources of errors. And when researching natural fibres, surface treatments, or experimental materials, this scale might not be possible when materials are scarce. Then it might be wise to change to microscale measurements.
Micromechanical testing has been around for decades, and there are multiple methods to measure the interfacial shear strength (IFSS) of a fibre matrix interface. The most well-known methods are microbond, pull-out, and single fibre fragmentation tests. These methods have had problems, either in sample manufacturing or getting consistent results from the measurements. There is a rising interest at the moment in multiscale modeling, where microscale properties of the material are the starting point for understanding the laminate behavior of composite materials. Industries are also interested in new materials, e.g., natural fibres, lignin and/or cellulose-based carbon fibres, vitrimers, and thermoplastic composites. These have increased the need for rapid testing equipment, which does not require pilot-scale production of materials. The aforementioned drivers have been one of the key factors in the development of FIBRODrop and FIBROBond devices. They can create a large set of samples from a small amount of material and measure them in a hasty schedule.
FIBRODrop and FIBROBond devices together are an excellent choice for developing sizings, characterization of new materials, comparison of batches of products, or determining the material properties for models. For testing, you would only need ~30 cm of rowing or tow and 100g of thermoset resin (or a few granulates of thermoplastic polymer) to perform the microbond measurements.
So, why would you waste your time measuring on macroscale and just save your energy and start measuring in microscale today? Hit us with a call, email, fill out the form or follow us @linkedin and we will guide you toward microscale measurements.
Fibrobotics is honored to be one of the 6 companies that were admitted into the European Space Agency - ESA - Business Incubator program this summer. These startups will receive funding from ESA-BIC and Business Finland, and will receive support from ESA-BIC and Aalto Startup Center.
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Especially the applications of fibrous composites in miniature products, dental and other medical applications require accurate data of microscale mechanics. The characterization of adhesion between single filament and picoliter-scale polymer matrix usually relies on the experiments using so-called microbond (MB) testing. In this paper, a monolithic compliant based structure with an integrated Fiber Bragg Grating (FBG) sensor is developed and analysed. The developed strain-sensing CBPM-FBG holder shows excellent sensitivity during the MB tests for both synthetic and natural filaments, even at a low filament diameters as low as 7μm, making the monolithic compliant structure the first instrument capable of force-strain data output for bonded filament-droplet specimens.
"For the first time ever, force-filament strain data was systematically collected for droplet-filament specimens at a sampling rate of ≥50Hz when the integral FBG-CBPM specimen holder was operated in the Fibrobond MB tester; three different material systems were analyzed. FEA of the MB testing with glass filaments and epoxy droplets enabled fitting and exact interfacial CZM-based debond model and frictional sliding with a friction coefficient of 0.35." (Royson et al. 2021)
