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Organisationseinheit der BAM
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Paper des Monats
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Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.
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.
Thermographic super resolution techniques allow the spatial resolution of defects/inhomogeneities below the classical limit, which is governed by the diffusion properties of thermal wave propagation. In this work, we report on the extension of this approach towards a full frame 2D super resolution technique. The approach is based on a repeated spatially structured heating using high power lasers. In a second post-processing step, several measurements are coherently combined using mathematical optimization and taking advantage of the (joint) sparsity of the defects in the sample
Thermographic super resolution techniques allow the spatial resolution of defects/inhomogeneities below the classical limit, which is governed by the diffusion properties of thermal wave propagation. In this work, we re-port on the extension of this approach towards a full frame 2D super resolution technique. The approach is based on a repeated spatially structured heating using high power lasers. In a second post-processing step, several measurements are coherently combined using mathematical optimization and taking advantage of the (joint) sparsity of the defects in the sample.
The Alpine Fault zone in New Zealand marks a major transpressional plate boundary that is late in its typical earthquake cycle. Understanding the subsurface structures is crucial to understand the tectonic processes taking place. A unique seismic survey including 2D lines, a 3D array, and borehole recordings, has been performed in the Whataroa Valley and provides new insights into the Alpine Fault zone down to ∼2 km depth at the location of the Deep Fault Drilling Project (DFDP)-2 drill site. Seismic images are obtained by focusing prestack depth migration approaches. Despite the challenging conditions for seismic imaging within a sediment filled glacial valley and steeply dipping valley flanks, several structures related to the valley itself as well as the tectonic fault system are imaged. A set of several reflectors dipping 40°–56° to the southeast are identified in a ∼600 m wide zone that is interpreted to be the minimum extent of the damage zone. Different approaches image one distinct reflector dipping at ∼40°, which is interpreted to be the main Alpine Fault reflector located only ∼100 m beneath the maximum drilled depth of the DFDP-2B borehole. At shallower depths (z < 0.5 km), additional reflectors are identified as fault segments with generally steeper dips up to 56°. Additionally, a glacially over-deepened trough with nearly horizontally layered sediments and a major fault (z < 0.5 km) are identified 0.5–1 km south of the DFDP-2B borehole. Thus, a complex structural environment is seismically imaged and shows the complexity of the Alpine Fault at Whataroa.
Additive Manufacturing (AM) in terms of laser powder-bed fusion (L-PBF) offers new prospects regarding the design of parts and enables therefore the production of complex structures. The quality of the feedstock material receives increasing attention, as it depicts the first part of the L-PBF process chain. The powder quality control in terms of flowability and powder bed packing density is therefore mandatory.
In this work, a workflow for quantitative 3D powder analysis in terms of particle size, particle shape, particle porosity, inter-particle distance and packing density was established. Synchrotron computed tomography (CT) was used to correlate the packing density with the particle size and particle shape for three different powder batches. The polydisperse particle size distribution (PSD) was transformed into a statistically equivalent bidisperse PSD. The ratio of the small and large particles helped to understand the powder particle packing density. While the particle shape had a neglectable influence, the particle size distribution was identified as major contributor for the packing density.
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.
The overview of the activity of Federal Institute for Material Research and Testing (BAM, Belin, Germany) in the field of additively manufacturing material characterization will be presented. The research of our group is focused on the 3D imaging of AM materials by means of X-ray Computed Tomography at the lab and at synchrotron, and the residual stress characterization by diffraction (nondestructive technique).
Metal Additive Manufacturing (AM) technologies such as Laser Powder Bed Fusion (LPBF) are characterized by layer wise construction, which enable advancements of component design, leading to potential efficiency and performance improvements. However, the rapid cooling rates associated with the process consequently leads to the generation of high magnitude residual stresses (RS). Therefore, a deep understanding of the formation of RS, the influence of process parameters on their magnitude and the impact on mechanical performance is crucial for widespread application. The experimental characterization of these RS is essential for safety related engineering application and supporting the development of reliable numerical models. Diffraction-based methods for RS analysis using high energy synchrotron X-rays and neutrons enable non-destructive spatially resolved characterization of both surface and bulk residual stresses in complex components. This presentation will provide an overview of recent research conducted by the BAM at large scale facilities for the characterization of residual stresses in LPBF metallic alloys. Special focus will be given to the challenges posed by textured LPBF materials for the reliable choice of the diffraction elastic constants (DECs), which is crucial to the accurate calculation of the level of RS.
The focus of the presentation focus will be on 3D imaging by means of X-ray Computed Tomography (XCT) at the lab and at synchrotron, and the non-destructive residual stress (RS) characterization by diffraction of additively manufactured (AM) materials in BAM (Berlin, Germany). The manufacturing defects and high RS are inherent of AM techniques and affect structural integrity of the components. Using XCT the defects size and shape distribution as well as geometrical deviations can be characterized, allowing the further optimization of the manufacturing process. Diffraction-based RS analysis methods using neutron and synchrotron X-rays at large scale facilities offer the possibility to non-destructively spatially resolve both surface and bulk RS in complex components and track their changes following applied thermal or mechanical loads.
The overview of the activity of group 8.5 Micro-NDT (BAM, Belin, Germany) in the field of additively manufacturing material characterization will be presented. The research of our group is focused on the 3D imaging of AM materials by means of X-ray Computed Tomography at the lab and at synchrotron, and the residual stress characterization by diffraction (nondestructive technique).
Absorption edge tomography, also known as differential tomography at absorption edges, is a method which exploits the sudden change of the attenuation coefficient, when the photon energy crosses the absorption edge of an element. Synchrotron radiation is the best source for absorption edge tomography, because of its small bandwidth, high intensity and easily adjustable photon energy. The synchrotron beamline BAMline at the synchrotron radiation facility BESSY II in Berlin, which is operated by the Bundesanstalt für Materialforschung und -prüfung (BAM), provides a monochromatized beam in a photon energy range from 5 keV up to 80 keV with a bandwidth of 2%, when the double multilayer monochromator is used. Together with the microtomography setup, this enables differential tomography with submicron resolution at the K edge of the elements from chromium up to the lanthanides, and up to uranium, when the L edges are used as well.
In this work, metal organic frameworks (MOFs) embedded in polymer are characterized using differential tomography. MOFs are microporous structures of metal ions, coordinated by organic linker molecules, that can be used in a broad field of applications, especially in gas storage and catalysis. In this work, polymer embedded MOFs were extruded into filaments, which could be used for 3d-printing to profit from the specific properties of the MOFs in polymeric materials combined with the arbitrary shapes provided by 3d-printing. For the extrusion, different polymer classes like ABS, polyester- and polyetherurethanes, as well as different MOFs (ZIF-8, ZIF-67, HKUST-1) were used to create MOF containing filaments with a nominal diameter of 3.0 mm. Differential tomography at the edges of the Zn, Co, and Cu was then used to find the distribution of the corresponding MOF in the filament and to analyze the shape of the inclusions.
The Alpine Fault at the West Coast of the South Island (New Zealand) is a major plate boundary that is expected to rupture in the next 50 years, likely as a magnitude 8 earthquake. The Deep Fault Drilling Project (DFDP) aimed to deliver insight into the geological structure of this fault zone and its evolution by drilling and sampling the Alpine Fault at depth. Here we present results from a seismic survey around the DFDP-2 drill site in the Whataroa Valley where the drillhole almost reached the fault plane. This unique 3D seismic survey includes several 2D lines and a 3D array at the surface as well as borehole recordings. Within the borehole, the unique option to compare two measurement systems is used: conventional three-component borehole geophones and a fibre optic cable (heterodyne Distributed Vibration Sensing system (hDVS)). Both systems show coherent signals but only the hDVS system allowed a recording along the complete length of the borehole.
Despite the challenging conditions for seismic imaging within a glacial valley filled with sediments and steeply dipping valley flanks, several structures related to the valley itself as well as the tectonic fault system are imaged. The pre-processing of the seismic data also includes wavefield separation for the zero-offset borehole data. Seismic images are obtained by prestack depth migration approaches.
Within the glacial valley, particularly steep valley flanks are imaged directly and correlate well with results from the P-wave velocity model obtained by first arrival travel-time tomography. Additionally, a glacially over-deepened trough with nearly horizontally layered sediments is identified about 0.5 km south of the DFDP-2B borehole.
With regard to the expected Alpine fault zone, a set of several reflectors dipping 40-56° to the southeast are identified in a ~600 m wide zone between depths of 0.2 and 1.2 km that is interpreted to be the minimum extent of the damage zone. Different approaches image one distinct reflector dipping at 40°, which is interpreted to be the main Alpine Fault reflector. This reflector is only ~100 m ahead from the lower end of the borehole. At shallower depths (z<0.5 km), additional reflectors are identified as fault segments and generally have steeper dips up to 56°. About 1 km south of the drill site, a major fault is identified at a depth of 0.1-0.5 km that might be caused by the regional tectonics interacting with local valley structures. A good correlation is observed among the separate seismic data sets and with geological results such as the borehole stratigraphy and the expected surface trace of the fault.
In conclusion, several structural details of the fault zone and its environment are seismically imaged and show the complexity of the Alpine Fault at the Whataroa Valley. Thus, a detailed seismic characterization clarifies the subsurface structures, which is crucial to understand the transpressive fault’s tectonic processes.
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.
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 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 are comparable in size according to the equivalent diameter. The packing density is lower (i.e. the powder bed contains more voids in between particles) for the Ti-6Al-4V particles. The comparison of the tomographic results with laser diffraction, as another particle size measurements techniques, proved agreement.
It can be difficult to efficiently model ultrasonic waves in 3D structures, especially when the computational model needs to account for complex geometries. This contribution presents a solution based on the Scaled Boundary Finite Element Method (SBFEM). It is a numerical tool suitable for elastodynamic problems. A space-tree discretisation, namely quad-trees, is used. This technique allows the decomposition of an image into quadrilaterals or quads, which are extruded to generate the 3D plate geometry. In particular, small quads resolve regions with discontinuities, allowing them to represent fine details in the structure. Moreover, this meshing technique allows for exploiting cell similarities, making the calculation procedure more efficient. The space-tree discretisations are generated from a high-resolution image containing all the information about damaged regions or boundary conditions. The resulting SBFEM polyhedral domains employ transition elements to ensure correct coupling between cells of different sizes. The analytical solution of a cylindrical scatterer serves as a reference to validate the proposed approach. Other examples also demonstrate the validity of the methodology and its flexibility.
The focus of the presentation focus will be on 3D imaging by means of X-ray Computed Tomography (XCT) at the lab and at synchrotron, and the non-destructive residual stress (RS) characterization by diffraction of different kind of materials in FB8.5 Micro-NDT BAM. For instance, the manufacturing defects and high RS are inherent of additively manufacturing techniques and affect structural integrity of the components. Using XCT the defects size and shape distribution as well as geometrical deviations can be characterized, allowing the further optimization of the manufacturing process. Diffraction-based RS analysis methods using neutron and synchrotron X-rays at large scale facilities offer the possibility to non-destructively spatially resolve both surface and bulk RS in complex components and track their changes following applied thermal or mechanical loads.
Higher energy density materials are being pushed by the research community to make lithium ion batteries a better competitor of chemical fossil fuels for transport applications. This increases potential risk of lithium ion batteries and therefore safety investigations are highly important for application purposes. Operando Computer Tomography provides a non-destructive investigation method of different abuse mechanisms.
Application of X-ray computed tomography (XCT) for studying lithium-ion batteries has gained interest among the research community especially in the past decade [1]. This technique is widely used for ex-situ samples to measure porosity and tortuosity [2], particle size and volume distribution [3] in the graphite anode as well as different cathode materials such as LiCoOx and NiMnCoOx. [4]. In situ measurements of commercial batteries are also often carried out to detect defects induced in a cell by a safety abuse test or manufacturing process [5]. Operando CT of large cells (for example 18650 form factor) is conducted at synchrotron facilities with high flux of high energy photons, however at a cost of details due to the large field of view [6].
Methodik
Thanks to their high brilliance, synchrotron beam facilitates us to do a full Computed Tomography in a short time. This enables us to measure batteries while being cycled with a reasonable time resolution to record morphological changes. In this poster we illustrate how one can utilize this ability to investigate abuse mechanisms on an actual commercially available lithium ion battery as well as a home made micro cell.
Ergebnisse
In this work, lab-based and synchrotron X-ray computed tomography is applied to commercial Li-ion batteries. It is shown how to find most suitable imaging settings to study available lithium-ion batteries on different size scales, from cell level to particle level. We also demonstrate how to optimize contrast as well as both temporal and spatial resolutions to study in-situ and operando processes in a commercial battery using attenuation and phase contrast SXCT. Manufacturing defects and inconsistencies on cell level as well as the electrode and microstructure on material level are shown in our study. Moreover, some abuse conditions are imaged in operando in a commercially available li-ion battery.
Diskussion
This work has demonstrated various imaging settings using lab and synchrotron based X-ray computed tomography to study in-situ as well as under operando condition, some abuse mechanisms in commercial lithium-ion batteries from cell level to electrode and particle level.
We demonstrate (and are the first to do so) 63 km Brillouin Optical Frequency-Domain Analysis (BOFDA) for temperature and strain monitoring using a 100 km fiber loop. The use of BOFDA for long-range applications can be considered a novel approach, as previous investigations focused on the utilization of Brillouin Optical Time-Domain Reflectometry and Analysis (BOTDR and BOTDA, respectively). At 51.7 km, a 100 m hotspot (37 °C) was detected without using distributed Raman amplification or image processing.
We present, to our knowledge for the first time, a 100-km Brillouin Optical Frequency-Domain Analysis (BOFDA) employing a 200-km fiber loop. Compared to our previous publication, enhanced sensor length, sensor accuracy and spatial resolution are presented. The performance improvements are achieved by applying distributed Raman amplification (DRA) and a digital high-pass filter. We report on temperature measurements over sensing distances of 75 km and 100 km both with a 12.5-m spatial resolution. Temperature changes of 5 °C have been measured along 75 km sensing fiber. A temperature change of 30 °C has been detected at 99.5 km.
In general, wind turbines transform the kinetic energy of the wind into electric power. Thereby, the wind turbine blades are facing unsteady loads which are transferred to the hub to generate a rotation of the turbine’s axis. This brief introduction focuses on the aerodynamics of the blades and the corresponding loads. Starting with the basic flow field and loads of an airfoil, terms like stagnation point, boundary layer, Reynolds number, transition, and separation are introduced. For different geometries, lift and drag coefficient curves are discussed. Then, full wings will be considered, including their three-dimensional flow field due to wing tip vortices and crossflows. As a main source of increased loads, unsteady effects are explained in more detail such as gusts, tower passing, earth boundary layer crossing, free stream turbulences, yaw misalignment, etc. At the end, extra loads due to an oscillating free stream are introduced.
The simulation of ultrasonic waves in a linearly elastic body can be computationally intensive. The reason is the relatively short wavelength compared to the body size for high frequencies. One possible approach to counteract the high computational costs is to decompose the domain into small parts and strive for parallelization. The Mortar Method is a well-established approach for domain decomposition.
A rather new approach to discretize the emerging subdomains is the Scaled Boundary Finite Element Method. This semi-analytical method has many attractive properties. Some of these properties are listed subsequently. The grid consists of polygonal elements, which leaves much freedom in the meshing process. A variety of material distributions, including anisotropic materials, can be considered. High-order shape functions can be used for optimal convergence properties. The approach treats singularities at crack tips and corners analytically. Especially in the frequency domain, the Scaled Boundary Finite Element Method reduces the dimension of the approximation because only degrees of freedom which are associated with the boundary of a polygonal element are necessary. Those desirable properties make the method particularly suitable for calculating the dynamic responses in bodies with cracks, as it is essential for many non-destructive testing and structural health monitoring applications.
In this contribution, we present a combination of the Scaled Boundary Finite Element Method with the Mortar Method in two dimensions. The presentation starts with a theoretical overview of both approaches. Subsequently, numerical examples demonstrate the stability of the combination for the polygonal boundary of the elements. The numerical examples increase in complexity and are compared to results computed on non-divided domains with the Finite Element Method.
The separation of two closely located defects in fields of Thermographic NDE is very challenging. The diffusive nature of thermal waves leads to a fundamental limitation in spatial resolution. Therefore, super resolution image reconstruction can be used. A new concerted ansatz based on spatially structured heating and joint sparsity of the signal ensemble allows an improved reconstruction of closely located defects. This new technique has also been studied using 1D laser arrays in active thermography.
The post-processing can be roughly described by two steps: 1. Finding a sparse basis representation using a reconstruction algorithm such as the Fourier transform, 2. Application of an iterative joint sparsity (IJOSP) method to the firstly reconstructed data. For this reason, different methods in post-processing can be compared using the same measured data set.
The focus in this work was the variation of reconstruction algorithms in step 1 and its influence on the results from step 2. More precise, the measured thermal waves can be transformed to virtual (ultrasound) waves that can be processed by applying ultrasound reconstruction algorithms and finally the super resolution algorithm. Otherwise, it is also possible to make use of a Fourier transform with a subsequent super resolution routine. These super resolution thermographic image reconstruction techniques in post-processing are discussed and evaluated regarding performance, accuracy and repeatability.
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.
As opposed to reviewing results on experimental determination of residual stress by diffraction, this paper discusses the open issues when dealing with residual stress determination in additive manufactured parts, in particular those manufactured with laser powder bed fusion techniques. Three points are addressed in detail: (a) the proper determination of the strain-free reference d0, (b) the problem of the determination of the principal axes, and (c) the use of the correct diffraction elastic constants. It is shown that all methods to determine the strain-free reference d0 suffer from caveats, and care must be taken in evaluating the most suitable for the problem being tackled. In addition, it is shown that, in some systems, the principal axes do correspond to the geometrical axes of the specimen, but this needs to be systematically checked, especially in the case of uni- or bidirectional hatching strategies. Finally, the need to experimentally determine the proper diffraction elastic constants is underlined, especially in the case of strongly textured specimens, which again depends on the deposition strategy.
The prediction of porosity is a crucial task for metal based additive manufacturing techniques such as laser powder bed fusion. Short wave infrared thermography as an in-situ monitoring tool enables the measurement of the surface radiosity during the laser exposure. Based on the thermogram data, the thermal history of the component can be reconstructed which is closely related to the resulting mechanical properties and to the formation of porosity in the part. In this study, we present a novel framework for the local prediction of porosity based on extracted features from thermogram data. The framework consists of a data pre-processing workflow and a supervised deep learning classifier architecture. The data pre-processing
workflow generates samples from thermogram feature data by including feature information from multiple subsequent layers.
Thereby, the prediction of the occurrence of complex process phenomena such as keyhole pores is enabled. A custom convolutional neural network model is used for classification. Themodel is trained and tested on a dataset from thermographic in-situ monitoring of the manufacturing of an AISI 316L stainless steel test component. The impact of the pre-processing parameters and the local void distribution on the classification performance is studied in detail. The presented model achieves an accuracy of 0.96 and an f1-Score of 0.86 for predicting keyhole porosity in small sub-volumes with a dimension of (700 × 700 × 50) μm3. Furthermore, we show that pre-processing parameters such as the porosity threshold for sample
labeling and the number of included subsequent layers are influential for the model performance. Moreover, the model prediction is shown to be sensitive to local porosity changes although it is trained on binary labeled data that disregards the actual sample porosity.
We present a temperature sensor based on a polymer exhibiting a Lower Critical Solution Temperature (LCST) in aqueous solution encapsulated in a capillary. Parameters are chosen such that the solution exhibits a cloud point in a temperature range of 30° C to 39°C. The characteristic of thermoresponsive polymers with an LCST, is that above that temperature phase separation of the polymer takes place which leads to a temperature-dependent formation of a cloudy suspension. An optical intensity measurement over the desired temperature range is established by an increase of optical attenuation inside the polymer solution caused by a rising temperature. For our purpose, the polymer capillary is connected to transmitter and receiver via a Polymer Optical Fibre (POF). Our intensity measurement is, to the best of our knowledge, a novel method and can be considered simple when compared to existing fibre-based temperature measurement techniques. Due to the lack of electrical components at the probe, this sensor is suitable for measurements in strong electromagnetic fields and environments for which flying sparks are hazardous, i.e., inflammable fluids or gases. Furthermore, all manufactured sensors share the same temperature dependence and, therefore, are well-suited for comparative measurement, e.g., flow measurement systems. With the given temperature range, a body temperature measurement is also suitable.
The Scaled Boundary Finite Element Method (SBFEM) for prismatic structures is an efficient method for the simulation of acoustic behavior. Hence a further development of the method is of great interest. The wave propagation can be calculated for isotropic and anisotropic materials in solids. As for many applications the acoustic behavior in fluids and the behavior in case of fluid-structure interaction (FSI) is subject of research, the implementation of a fluid model in SBFEM for prismatic structures is needed. In case of FSI the coupling between fluid and solid domains can be performed without additional effort when describing both domains in the same variables. Hence a displacement-based fluid description is used. As the discretized formulation leads to spurious modes, a penalty method to suppress the unphysical behavior is chosen. To validate the derived model a comparison with analytical solutions of purely fluid domains is made. As to verify that in case of FSI the model shows the right behavior, dispersion curves of water-filled pipes are calculated and compared to results obtained with Comsol.
The Scaled Boundary Finite Element Method is known as an efficient method for the simulation of ultrasonic wave propagation. As to investigate acoustic wave behavior in case of fluid‐structure interaction, a fluid model is implemented in the SBFEM for prismatic structures. To omit coupling terms a displacement‐based formulation is used. Spurious modes, which occur in the solution, are suppressed using a penalty parameter. To verify this formulation dispersion curves obtained with Comsol Multiphysics are compared to results of SBFEM. The results of both methods are in very good agreement
By means of a small-scale truss bridge, the ability of the Measurement- and Model-based Structural Analysis to detect and localize damage was examined. Although there was no noteworthy difficulty in detecting damage, it turned out that damage localization responds sensitively to systematic influences, i.e. non-modelled properties of the mechanical model. Therefore, another experiment is being conducted to re-examine the Measurement- and Model-based Structural Analysis. For this purpose, the bending test is carried out as it has been already theoretically respectively numerically discussed. In this attempt, the systematic influences such as residual stress are kept as low as possible.
By means of a small-scale truss bridge, the ability of the Measurement- and Model-based Structural Analysis to detect and localize damage was examined. Although there was no noteworthy difficulty in detecting damage, it turned out that damage localization responds sensitively to systematic influences, i.e. non-modelled properties of the mechanical model. Therefore, another experiment is being conducted to re-examine the Measurement- and Model-based Structural Analysis. For this purpose, the bending test is carried out as it has been already theoretically respectively numerically discussed. In this attempt, the systematic influences such as residual stress are kept as low as possible.
By means of a small-scale truss bridge, the ability of the Measurement- and Model-based Structural Analysis to detect and localise damage was examined in. Although there was no noteworthy difficulty in detecting damage, it turned out that damage localisation responds sensitively to systematic influences, i.e. non-modelled properties of the mechanical model. Therefore, another experiment is being conducted to re-examine the Measurement- and Model-based Structural Analysis. For this purpose, the bending test is carried out as it has been already theoretically respectively numerically discussed in. In this attempt, the systematic influences such as residual stress are kept as low as possible.
The reuse of existing foundations is considered in various construction Projects to save time and costs while avoiding unnecessary interference with other underground objects. Because the design and as-built drawings might not be complete and questions may arise regarding the condition of the foundations, a detailed investigation is a prerequisite for the planning process in most cases. Nondestructive testing (NDT) techniques are a core part of this endeavor.
The processes and procedure for foundation reuse planning are not yet standardized, and the possibilities and limitations of NDT methods are not known to many planners and stakeholders. The German research Project REFUND (2014–2016) has developed charts for the planning and Investigation process that consider available standards and the current state of the art in NDT.
These charts are separately available for single and strip, slab, and pile foundations. Available NDT methods have been compiled and evaluated for specific tasks at these foundation types, including their respective limitations.
The procedures have been successfully tested in two real-world projects. The results from these projects enable planners to improve the reliability of the process while potentially saving significant resources. This paper discusses the procedures for pile foundations and demonstrates the use of various NDT methods in a project on the reuse of electrical tower foundations.
This paper presents an approach to the automatic enrichment of finite elements in the vicinity of a stress singularity. The enrichment consists of semi-analytical singular modes constructed using the Scaled Boundary Finite Element Method (SBFEM).
In contrast to analytical methods, the SBFEM provides modes for inhomogeneous and anisotropic materials without additional effort. The finite element basis can be of arbitrary order and remains unaltered by the enrichment. The approach requires enrichment in only one layer of elements around a node. Due to the compatibility of SBFEM with FEM, there is no Need for transitional elements, and there are no parasitic terms. The approach is tested for several benchmark problems. The stress intensity factors are computed based on techniques inspired by the SBFEM. The proposed procedure is compared to a Standard finite element implementation and shows a significant improvement in the error of the displacement field for problems involving singular stresses.
The applications of composite overwrapped pressure vessels (COPVs) in extreme conditions, such as storing hydrogen gases at very high pressure, impose new requirements related to the system's integrity and safety. The development of a structural health monitoring (SHM) system that allows for continuous monitoring of the COPVs provides rich information about the structural integrity of the component. Furthermore, the collected data can be used for different purposes such as increasing the periodic inspection intervals, providing a remaining lifetime prognosis, and also ensuring optimal operating conditions. Ultimately this information can be complementary to the development of the envisioned digital twin of the monitored COPVs. Guided waves (GWs) are preferred to be used in continuous SHM given their ability to travel in complex structures for long distances. However, obtained GW signals are complex and require advanced processing techniques. Machine learning (ML) is increasingly utilized as the main part of the processing pipeline to automatically detect anomalies in the system's integrity. Hence, in this study, we are scrutinizing the potential of using ML to provide continuous monitoring of COPVs based on ultrasonic GW data. Data is collected from a network of sensors consisting of fifteen Piezoelectric (PZT) wafers that were surface mounted on the COPV. Two ML algorithms are used in the automated evaluation procedure (i) a long short-term memory (LSTM) autoencoder for anomaly detection (defects/impact), and (ii) a convolutional neural network (CNN) model for feature extraction and classification of the artificial damage sizes and locations. Additional data augmentation steps are introduced such as modification and addition of random noise to original signals to enhance the model's robustness to uncertainties. Overall, it was shown that the ML algorithms used were able to detect and classify the simulated damage with high accuracy.
The applications of composite overwrapped pressure vessels (COPVs) in extreme conditions, such as storing hydrogen gases at very high pressure, impose new requirements related to the system's integrity and safety. The development of a structural health monitoring (SHM) system that allows for continuous monitoring of the COPVs provides rich information about the structural integrity of the component. Furthermore, the collected data can be used for different purposes such as increasing the periodic inspection intervals, providing a remaining lifetime prognosis, and also ensuring optimal operating conditions. Ultimately this information can be complementary to the development of the envisioned digital twin of the monitored COPVs. Guided waves (GWs) are preferred to be used in continuous SHM given their ability to travel in complex structures for long distances. However, obtained GW signals are complex and require advanced processing techniques. Machine learning (ML) is increasingly utilized as the main part of the processing pipeline to automatically detect anomalies in the system's integrity. Hence, in this study, we are scrutinizing the potential of using ML to provide continuous monitoring of COPVs based on ultrasonic GW data. Data is collected from a network of sensors consisting of fifteen Piezoelectric (PZT) wafers that were surface mounted on the COPV. Two ML algorithms are used in the automated evaluation procedure (i) a long short-term memory (LSTM) autoencoder for anomaly detection (defects/impact), and (ii) a convolutional neural network (CNN) model for feature extraction and classification of the artificial damage sizes and locations. Additional data augmentation steps are introduced such as modification and addition of random noise to original signals to enhance the model's robustness to uncertainties. Overall, it was shown that the ML algorithms used were able to detect and classify the simulated damage with high accuracy.
This work presents machine learning-inspired data fusion approaches to improve the non-destructive testing of reinforced concrete. The principal effects that are used for data fusion are shown theoretically. Their effectiveness is tested in case studies carried out on largescale concrete specimens with built-in chloride-induced rebar corrosion. The dataset consists of half-cell potential mapping, Wenner resistivity, microwave moisture and ground penetrating radar measurements. Data fusion is based on the logistic Regression algorithm.
It learns an optimal linear decision boundary from multivariate labeled training data, to separate intact and defect areas. The training data are generated in an experiment that simulates the entire life cycle of chloride-exposed concrete building parts. The unique possibility to monitor the deterioration, and targeted corrosion initiation, allows data labeling.
The results exhibit an improved sensitivity of the data fusion with logistic regression compared to the best individual method half-cell potential.
A concept for the application of a magnetocaloric device in energy-efficient air conditioners is introduced. In order to evaluate this concept, a test stand has been developed equipped with a magnetic field source providing about a 1.5-T flux density change into which different Regenerator geometries can be implemented and evaluated. A processing route for the production of profiled magnetocaloric LaFeSiMn-based composite plates by tape casting is presented. The processed plates show a maximum isothermal entropy change of about 3.8 J kg−1 K−1 at a magnetic field change of 1.5 T at 285 K. The hydraulic and thermal performance of regenerator geometries that can be realized by profiled plates is assessed by calculations.
Fiber-reinforced concretes (FRCs) offer significant improvements in tensile strength and durability compared to most other concrete mixes.
However, for safe and efficient use of FRC in large structures, anisotropy of fiber orientation needs to be understood and properly controlled. In this project, both cored samples extracted from a FRC slab and FRC samples cast individually in molds were assessed using X-ray computed tomography (CT) and measurements of fiber orientation were extracted from the resulting CT images. These results showed that fibers within the slab were highly anisotropic in orientation while fibers in individually cast samples showed a much more heterogeneous distribution of orientations.
This indicates that fiber orientation is highly dependent on the casting process and suggests that FRC can only be safely and efficiently utilized if anisotropic fiber orientation is properly accounted for during design and optimized casting methods are used during construction.
Attenuation of ultrasonic signals in concrete has the potential to carry much information about the microstructure of the material. In this work a series of concrete specimens of varying porosities and pore size distributions were internally imaged with x-ray computed tomography (CT), and then subsequently examined with throughtransmission ultrasound. The CT images were used to quantify both capillary porosity of cement paste as well as internal interfaces that are likely to produce elastic wave scattering. Ultrasound signals were represented as a diffusion process such that absorption and scattering attenuation could be isolated. As implemented, the diffusion model was a poor predictor of capillary porosity, while diffusivity was a reasonable predictor of scattering interfaces. Diffusivity was found to scale extremely well with small scale porosity, which made it a good predictor of compressive strength.
Reliability assessment of Structural Health Monitoring (SHM) systems
poses new challenges pushing the research community to address many questions which are still open. For guided wave-based SHM it is not possible to evaluate the system performance without taking into account the target structure and applied system parameters. This range of variables would result in countless measurements.
Factors like environmental conditions, structural dependencies and wave characteristics demand novel solutions for performance analysis of SHM systems compared to those relying on classical non-destructive evaluation. Such novel approaches typically require model-assisted investigations which may not only help to explain and understand performance assessment results but also enable complete studies without costly experiments. Within this contribution, a multi input multi output approach using a sparse transducer array permanently installed on a composite structure to excite and sense guided waves is considered. Firstly, the method and the analysis of path-based performance assessment are presented considering an open-access dataset from the Open Guided Wave platform. Then, a performance analysis of a guided wave-based SHM system using Probability of Detection is presented. To explain some unexpected results, the model-assisted investigations are used to understand the physical phenomena of wave propagation in the test specimen including the interaction with damage. Finally, issues and future steps in SHM systems’ performance assessment and their development are discussed.
Additive Manufacturing (AM) in terms of laser powder-bed fusion (L-PBF) offers new prospects regarding the design of parts and enables therefore the production of lattice structures. These lattice structures shall be implemented in various industrial applications (e.g. gas turbines) for reasons of material savings or cooling
channels. However, internal defects, residual stress, and structural deviations from the nominal geometry are unavoidable.
In this work, the structural integrity of lattice structures manufactured by means of L-PBF was non-destructively investigated on a multiscale approach.
A workflow for quantitative 3D powder analysis in terms of particle size, particle shape, particle porosity, inter-particle distance and packing density was established.
Synchrotron computed tomography (CT) was used to correlate the packing
density with the particle size and particle shape. It was also observed that at least about 50% of the powder porosity was released during production of the struts.
Struts are the component of lattice structures and were investigated by means of laboratory CT. The focus was on the influence of the build angle on part porosity and surface quality. The surface topography analysis was advanced by the quantitative characterisation of re-entrant surface features. This characterisation was compared with conventional surface parameters showing their complementary
information, but also the need for AM specific surface parameters.
The mechanical behaviour of the lattice structure was investigated with in-situ CT under compression and successive digital volume correlation (DVC). The Deformation was found to be knot-dominated, and therefore the lattice folds unit cell layer wise.
The residual stress was determined experimentally for the first time in such lattice structures. Neutron diffraction was used for the non-destructive 3D stress investigation. The principal stress directions and values were determined in dependence of the number of measured directions. While a significant uni-axial stress state was found in the strut, a more hydrostatic stress state was found in the knot. In both cases, strut and knot, seven directions were at least needed to
find reliable principal stress directions.
A systematic, nature-inspired chemical engineering approach is employed to solve the issue of flooding in electrochemical devices. The mechanism of passive water transport utilized by lizards living in arid environments is leveraged to design flow-fields with a microchannel structure on their surface, through which capillary pressure rapidly removes the water generated in the electrochemical device. This water management strategy is implemented in proton exchange membrane fuel cells (PEMFCs) with a lunginspired flow-field, which ensures uniform distribution of reactants across the catalyst layer. Jointly, this nature-inspired approach results in flood-free, stable operation at 100% RH and a B60% increase in current (B1.9 A cm-2) and peak power density (B650 mW cm−2) compared to current PEMFCs with a flood-prone, serpentine flow-field (B0.8 A cm-2 and 280 mW cm-2, respectively). This significant advance allows for PEMFC operation at fully humidified conditions.
We demonstrate the use of a 3D printed radial collimator in X-ray powder diffraction and surface sensitive grazing incidence X-ray diffraction. We find a significant improvement in the overall Signal to background ratio of up to 100 and a suppression of more than a factor 3⋅10⁵ for undesirable Bragg reflections generated by the X-ray “transparent” windows of the sample environment.
The background reduction and the removal of the high intensity signals from the windows, which limit the detector’s dynamic range, enable significantly higher sensitivity in experiments within sample environments such as vacuum chambers and gas- or liquid-cells. Details of the additively manufactured steel collimator geometry, alignment strategies using X-ray fluorescence, and data analysis are also briefly discussed. The flexibility and affordability of 3D prints enable designs optimized for specific detectors and sample environments, without compromising the degrees of freedom of the diffractometer.
A novel approach for rating fatigue-initiating inclusions in highly demanding steel (INCAFAT)
(2018)
INCAFAT project aimed to improve existing fatigue damage models by establishing the most suitable combination of measurement techniques to characterise harmful inclusion populations in highly demanding steels. The different inclusion assessments carried out confirm that, chemical composition, secondary metallurgy and manufacturing route affect content, nature, size and shape of inclusions. According to the FEM model, inclusions produce an alteration of the stress field in their surrounding region, which can promote a fatigue failure. Experimental work on fatigue testing has demonstrated that depending on the stressed direction fatigue failures in highly demanding steels could be produced by different types of inclusions. Fractography analyses confirmed that meso-inclusions harmful in fatigue cannot be rated by standard methods, nor 10 MHz ultrasonic testing (macro) or micro-cleanness assessments. The necessity of rating these meso-inclusions has led to critical evaluation of Extreme Value Analysis according to ASTM E2283-08 and the development of high frequency immersion ultrasonic testing. EVA methodology based on inclusion width can be applied reliably when principal stress is parallel to the rolling direction. On the contrary, if inclusions are testing in the elongated directions its fails. On the other hand, the guidelines and recommendations for high frequency ultrasonic testing have been compiled in a new European standard draft. This method based on focal beam probes and high-resolution devices is able to provide information on meso-inclusion distribution.
We propose a novel iterative segmentation algorithm (i.S.Sy.Da.T.A: Iterative Segmentation Synthetic Data Training Algorithm) employing Deep Convolutional Neural Networks and synthetic training data for X-ray tomographic reconstructions of complex microstructures. In our method, we reinforce the synthetic training data with experimental XCT datasets that were automatically segmented in the previous iteration. This strategy produces better segmentations in successive iterations. We test our algorithm with experimental XCT re constructions of a 6-phase Al-Si Matrix Composite reinforced with ceramic fibers and particles. We perform the analysis in 3D with a special network architecture that demonstrates good generalization with synthetic training data. We show that our iterative algorithm returns better segmentations compared to the standard single training approach. More specifically, phases possessing similar attenuation coefficients can be better segmented: for Al2O3 fibers, SiC particles, and Intermetallics, we see an increase of the Dice score with respect to the classic approach: from 0.49 to 0.54, from 0.66 to 0.72, and from 0.55 to 0.66 respectively. Furthermore, the overall Dice score increases from 0.77 to 0.79. The methods presented in this work are also applicable to other materials and imaging techniques.
A paradigm shift in the description of creep in metals can only occur through multi-scale imaging
(2022)
The description of creep in metals has reached a high level of complexity; fine details are revealed by all sorts of characterization techniques and different theoretical models. However, to date virtually no fully microstructure-driven quantitative description of the phenomenon is available. This has brought to interesting inconsistencies; the classic description of (secondary) creep rests on the so-called power law, which however: a- has a pre-factor spanning over 10 orders of magnitude; b- has different reported exponents for the same material; c- has no explanation for the values of such exponents.
Recently, a novel description (the so-called Solid State Transformation Creep (SSTC) Model) has been proposed to tackle the problem under a different light. The model has two remarkable features: 1- it describes creep as the accumulation of elementary strains due to dislocation motion; 2- it predicates that creep is proceeding by the evolution of a fractal arrangement of dislocations. Such description, however, needs a great deal of corroborating evidence, and indeed, is still incomplete.
To date, we have been able to observe and somehow quantify the fractal arrangement of microstructures through Transmission Electron Microscopy (TEM), observe the accumulation of dislocations at grain boundaries by EBSD-KAM (Electron Back-Scattered Diffraction-Kernel Angular Misorientation) analysis, quantify the kinetic character (solid state transformation) of experimental creep curves, and estimate the sub-grain size of the fractal microstructure through X-ray refraction techniques. All pieces of the mosaic seem to yield a consistent picture: we seem being on the right path to reconstruct the whole elephant by probing single parts of it. What is still missing is the bond between the various scales of investigation.
The amount of data generated worldwide is constantly increasing. These data come from a wide variety of sources and systems, are processed differently, have a multitude of formats, and are stored in an untraceable and unstructured manner, predominantly in natural language in data silos. This problem can be equally applied to the heterogeneous research data from materials science and engineering. In this domain, ways and solutions are increasingly being generated to smartly link material data together with their contextual information in a uniform and well-structured manner on platforms, thus making them discoverable, retrievable, and reusable for research and industry. Ontologies play a key role in this context. They enable the sustainable representation of expert knowledge and the semantically structured filling of databases with computer-processable data triples.
In this perspective article, we present the project initiative Materials-open-Laboratory (Mat-o-Lab) that aims to provide a collaborative environment for domain experts to digitize their research results and processes and make them fit for data-driven materials research and development. The overarching challenge is to generate connection points to further link data from other domains to harness the promised potential of big materials data and harvest new knowledge.