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Paper des Monats
- ja (1)
Eingeladener Vortrag
- nein (46)
The combination of microstructural data with other experimental techniques and with modeling is paramount, if we want to extract the maximum amount of information on porous material properties. In particular, quantitative image analysis, statistical approaches, direct discretization of tomographic reconstructions represent concrete possibilities to extend the power of the tomographic 3D representation to insights into the material and component performance. I will show a few examples of possible use of X-ray tomographic data for quantitative assessment of porosity in ceramics.
Moreover, I will show how not-so-novel 2D characterization techniques, based X-ray refraction, can allow a great deal of insights in the damage evolution in microcracked (and porous) ceramics. I will show how X-ray refraction can detect objects (e.g. microcracks) below its own spatial resolution.
Finally, I will discuss the link between the microstructural findings and the mechanical properties of porous microcracked ceramics.
The combination of microstructural data with other experimental techniques and with modeling is paramount, if we want to extract the maximum amount of information on porous material properties. In particular, quantitative image analysis, statistical approaches, direct discretization of tomographic reconstructions represent concrete possibilities to extend the power of the tomographic 3D representation to insights into the material and component performance. I will show a few examples of possible use of X-ray tomographic data for quantitative assessment of porosity in ceramics.
Moreover, I will show how not-so-novel 2D characterization techniques, based X-ray refraction, can allow a great deal of insights in the damage evolution in microcracked (and porous) ceramics. I will show how X-ray refraction can detect objects (e.g. microcracks) below its own spatial resolution.
Finally, I will discuss the link between the microstructural findings and the mechanical properties of porous microcracked ceramics.
The mixture of ammonium nitrate (AN) prills and fuel oil (FO), usually called ANFO, is extensively used in the mining industry as a bulk industrial explosive. One of the major performance predictors of ANFO mixtures is the fuel oil retention, which is itself governed by the porosity of the AN prills. Standardised tests routinely used to assess oil retention face several important limitations; the first being the difficulty to cover the wide range of porosity contents and morphologies from different types of ammonium nitrate prills; the second being the inability to evaluate the closed porosity, which is an important factor regarding the sensitivity of the explosive to detonation. In this study, we present how X-ray computed tomography (XCT), and the associated advanced data processing workflow, can be used to fully characterise the structure and morphology of AN prills. We show that structural parameters such as volume fraction of the different phases and morphological parameters such as specific surface area and shape factor can be reliably extracted from the XCT data, and that there is a good agreement with the measured oil retention values. XCT can therefore be employed to non-destructively and accurately evaluate and characterise porosity in ammonium nitrate prills.
Layer-by-layer additive manufacturing (AM) by means of laser-powder bed Fusion (L-PBF) offers many prospects regarding the design of lattice structures used, for example, in gas turbines. However, defects such as bulk porosity, Surface roughness, and re-entrant features are exacerbated in nonvertical structures, such as tilted struts. The characterization and quantification of these kinds of
defects are essential for the correct estimation of fracture and fatigue properties.
Herein, cylindrical struts fabricated by L-PBF are investigated by means of X-ray computed tomography (XCT), with the aim of casting light on the dependence of the three kinds of defects (bulk porosity, surface roughness, and re-entrant features) on the build angle. Innovative analysis methods are proposed to correlate shape and position of pores, to determine the angular-resolved Surface roughness, and to quantify the amount of re-entrant surface features, q. A meshing of the XCT surface enables the correlation of q with the classical Surface roughness Pa. This analysis leads to the conclusion that there is a linear correlation between q and Pa. However, it is conjectured that there must be a threshold of surface roughness, below which no re-entrant features can be build.
3D Crack analysis in hydrogen charged lean duplex stainless steel with synchrotron refraction CT
(2016)
Hydrogen in metals can cause a degradation of the mechanical properties, the so-called hydrogen embrittlement. In combination with internal stresses, hydrogen assisted cracking (HAC) can occur. This phenomenon is not completely understood yet. To better characterise the cracking behaviour, it is important to gain information about the evolution of the 3D crack network. For this purpose samples of lean duplex stainless steel were loaded with hydrogen by means of electrochemical charging and investigated by means of synchrotron refraction CT and SEM fractography after uniaxial tensile loading. Synchrotron refraction CT is an analyser-based imaging (ABI) technique. It uses a Si (111) single crystal as analyser, which is placed into the beam path between sample and detector. According to Bragg’s law only incident x-rays within a narrow range around the Bragg-angle are diffracted from the analyser into the detector. Hence, the analyser acts as an angular filter for the transmitted beam. This filtering allows to turn the refraction and scattering of x-rays into image contrast. Refraction occurs at all interfaces, where the density of the material changes and is more sensitive to density changes than the attenuation. Therefore, it is possible to detect smaller cracks than with classical x-ray imaging techniques, like CT, with comparable spacial resolution. It also visualises the 3D structure of the cracks and gains quantitative information about their morphology and distribution. Since cracks introduced by HAC are usually very small and have a small opening displacement, synchrotron refraction CT is expected to be well suited for imaging this cracking mechanism and can be a valuable tool to characterise the formation and the evolution of a 3D crack network.
Hydrogen in metals can cause a degradation of the mechanical properties with possible subsequent hydrogen assisted cracking (HAC). Though, the mechanism of HAC is not completely understood yet and thus suitable methods for in situ investigations to characterise the crack formation are needed. X-ray computed tomography (CT) is a well-known tool for analysing these properties. However, the effective resolution of the detector system limits the detection of small defects by CT. Analyser based imaging (ABI) takes advantage of x-ray refraction at interfaces between volumes of different density, i.e. of cracks, pores, inclusions, etc., within the sample to detect defects smaller than the resolution of the detector system. In this study, measurements on an aluminium alloy weld showed that ABI allows us to resolve the 3D structure of cracks undetected by absorption based CT. Prospective investigations will analyse HAC in steels.
To better understand the mechanism of hydrogen assisted cracking (HAC), it is important to investigate the 3D structure of the cracks non-destructively. Since, cracks introduced by HAC are usually very small, conventional x-ray imaging methods often lack the required spatial resolution.
However, the detection of those cracks can be enhanced by taking advantage of refraction at interfaces within the sample.
To image this refractive deflection we employ analyser based imaging (ABI). In this work we aim at proving the enhanced crack detection of ABI by investigating an alluminum alloy weld.
The quality of components made by laser beam melting (LBM) additive manufacturing is naturally influenced by the quality of the powder bed. A packing density <1 and porosity inside the powder particles lead to intrinsic voids in the powder bed. Since the packing density is determined by the particle size and shape distribution, the determination of these properties is of significant interest to assess the printing process. In this work, the size and shape distribution, the amount of the particle’s intrinsic porosity, as well as the packing density of micrometric powder used for LBM, have been investigated by means of synchrotron X-ray computed tomography (CT). Two different powder batches were investigated: Ti–6Al–4V produced by plasma atomization and stainless steel 316L produced by gas atomization. Plasma atomization particles were observed to be more spherical in terms of the mean anisotropy compared to particles produced by gas atomization. The two kinds of particles were comparable in size according to the equivalent diameter. The packing density was lower (i.e., the powder bed contained more voids in between particles) for the Ti–6Al–4V particles. The comparison of the tomographic results with laser diffraction, as another particle size measurement technique, proved to be in agreement.
It is shown that preconditioning of experimental X‐ray computed tomography (XCT) data is critical to achieve high‐precision segmentation scores. The challenging experimental XCT datasets and deep convolutional neural networks (DCNNs) are used that are trained with low‐resemblance synthetic XCT data. The material used is a 6‐phase Al–Si metal matrix composite‐reinforced with ceramic fibers and particles. To achieve generalization, in our past studies, specific data augmentation techniques were proposed for the synthetic XCT training data. In addition, two toolsets are devised: (1) special 3D DCNN architecture (3D Triple_UNet), slicing the experimental XCT data from multiple views (MultiView Forwarding), the i.S.Sy.Da.T.A. iterative segmentation algorithm, and (2) nonlocal means (NLM) conditioning (filtering) for the experimental XCT data. This results in good segmentation Dice scores across all phases compared to more standard approaches (i.e., standard UNet architecture, single view slicing, standard single training, and NLM conditioning). Herein, the NLM filter is replaced with the deep conditioning framework BAM SynthCOND introduced in a previous publication, which can be trained with synthetic XCT data. This leads to a significant segmentation precision increase for all phases. The proposed methods are potentially applicable to other materials and imaging techniques.