12th Conference on Industrial Computed Tomography, Fürth, Germany (iCT 2023), www.ict2023.org Ex and in situ tests of materials: From design to materials parameters via motion estimation Tessa Nogatz 1 , Claudia Redenbach 1 , Katja Schladitz 2 1 Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU), Gottlieb-Daimler-Straße 47, 67663 Kaiserslautern, Germany, e-mail: [email protected], [email protected] 2 Fraunhofer-Institut für Techno- und Wirtschaftsmathematik, Fraunhofer-Platz 1, 67663 Kaiserslautern, Germany, e-mail: [email protected] Abstract Materials tests are inevitable to investigate how materials behave under load. This also holds for materials that are not manufac- tured from bulk material but exhibit an interesting microstructure. Unfortunately, in this case classical stress-strain investigations are no longer sufficient to deduce a proper description of failure mechanisms. To overcome this problem, materials testing is usually combined with an imaging modality to gain information on local material behavior. The combination with classical digital cameras is quite common, but only yields surface information. Here, we present a framework to combine mechanical tests with computed tomography. Such in situ tests are not new, however, we also give information on how to perform tests ex situ. Our special focus is on motion estimation. We use an algorithm that is particularly suitable for delicate behavior of materials with interesting microstructures such as foams. The whole pipeline for materials tests, either in situ or ex situ, is outlined using two foam examples. 1 Introduction Digital Image Correlation (DIC) is a widely accepted tool to assess local mechanical behavior within a materials test. Part of its success stems from the fact that it can be integrated into nearly every materials test with manageable effort: Large-scale applications, high or low temperature or complex loading devices do not pose a problem as long as there is enough room for one or two high-speed cameras. Moreover, there exist guidelines and strategies to produce valuable and reliable results [11]. Much effort has been put into improvement of the method, not only algorithmically, but also by using cameras that record photos at higher speed with higher resolution. However, one cannot belie the fact that DIC yields reliable information only on the surface of the specimen that is tested. No matter how many cameras are used, the material’s behavior in the inner layers remains obscured. This may not cause problems when the material is relatively homogeneous. If however a material features a more complex microstructure which governs the material’s behavior, then considering only surface strains does not yield a sufficient description of the material. Concrete for example seems to be a dense, homogeneous material on a macroscopic scale, but exhibits a delicate microstructure with various phases and inclusions of different types. The influence is by far not trivial: In compression tests, cracks may form in the interior of test specimens. When they become visible on the outside, it is often too late to observe the phase of crack initiation. Yet the most interesting stage is initiation, as it gives information about the durability of concrete components. Another example where the local behavior has severe influence on the global material is porous, cellular structures. Metal foams belong to that category and in the literature their compressive behavior is described by three macroscopic stages: An initial elastic regime is followed by a plastic collapse of layers, before eventually one observes global material densification. Microscopically, this scheme is often already violated at the very beginning: Though initially the stress-strain curve shows a linear trend and therefore indicates elastic behavior, one can already observe single breaking struts. These failed struts are often a forerunner to the next stage, the plastic collapse of whole layers. It is therefore inevitable to develop materials tests and evaluation methods that unveil this small scale behavior. A commonly accepted tool for gaining insight into materials microstructures is computed tomography (CT). In the classical setup - laboratory tomographs that come with built-in routines for image reconstruction - it is unfortunately crucial that the specimen does not move during the scan. This is due to reconstruction routines not being externally accessible and being based on filtered backprojection which is very sensitive to motion during the scanning procedure. Nevertheless, it is possible to use CT to not only acquire digitized, three-dimensional representations of materials microstructures, but also time series of images displaying the evolution of the material under load. This is achieved by transforming mechanical tests from dynamic procedures to quasi-static ones, meaning that application of load is interrupted to obtain images of the intermediate, static stages of the experiment. Such a procedure is presented schematically in Figure 1. The main task is then to quantify the motion that the sample underwent during such a test. Receiving a digitized full displacement field from the materials test allows for diverse subsequent uses. Quantification of internal damage, crack opening and propagation, but also comparisons to simulations and material parameter identification have been reported [6]. In the following, we describe how to extract the motion that a sample underwent during a mechanical test from a sequence Copyright 2022 - by the Authors. Licensed under a Creative Commons Attribution 4.0 International License. More info about this article: https://www.ndt.net/?id=27752 https://doi.org/10.58286/27752
Ex and in situ tests of materials: From design to materials parameters via motion estimation
Jun 24, 2023
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