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For structural health monitoring systems or non-destructive testing, it is crucial to study the interactions of ultrasonic-guided waves with inhomogeneities or damage in structural components. Simulation of these interactions poses a challenge.
After the wave-damage interaction, the numerical model must reproduce features like mode conversion or wave scattering. Mathematically, damages are discontinuities in a computational domain, and each interacts diversely depending on its geometrical features. We propose a quadtree-based meshing in the current contribution to capture these geometrical
characteristics.
This kind of discretisation approach requires special techniques to couple cells of different sizes since, after a quadtree decomposition, the problem of so-called hanging nodes may arise. Transition elements based on high-order transfinite shape functions are used to solve this issue. The advantage is that these elements allow the correct coupling of the cells while retaining a high-order interpolation. Moreover, the reuse of the dynamic stiffness matrices can be exploited based on the similarity of the cells. This procedure renders the approach very efficient.
Examples show the scattering characteristics of different guided wave modes after interacting with inhomogeneities and discontinuities in a plate.
The overview of the activity of Federal Institute for Material Research and Testing (BAM, Belin, Germany) in the field material characterization by X-ray imaging is presented. The principle of X-ray Computed Tomography (XCT) is explained. The multiple examples of application of quantitative analysis by XCT are reported, such as additive manufacturing, Li-ion battery, concrete research.
To evaluate the durability of new alternative cement compositions, it is important to examine the internal transport of moisture through these materials. For this purpose, mortars were prepared from different types of cement and capillary suction experiments were carried out. The moisture transport was studied with an NMR tomograph and compared with weight easurements. With the tomograph, the total moisture input could be determined, as well as the moisture content within the samples non-destructively and spatially resolved. This allows precise observation of the moisture fronts. The tomograph was also used to determine the capillary transport
coefficient.
Laser powder bed fusion is one of the most promising additive manufacturing techniques for printing complex-shaped metal components. However, the formation of subsurface porosity poses a significant risk to the service lifetime of the printed parts. In-situ monitoring offers the possibility to detect porosity already during manufacturing. Thereby, process feedback control or a manual process interruption to cut financial losses is enabled.
Short-wave infrared thermography can monitor the thermal history of manufactured parts which is closely connected to the probability of porosity formation. Artificial intelligence methods are increasingly used for porosity prediction from the obtained large amounts of complex monitoring data. In this study, we aim to identify the potential and the challenges of deep-learning-assisted porosity prediction based on thermographic in-situ monitoring.
Therefore, the porosity prediction task is studied in detail using an exemplary dataset from the manufacturing of two Haynes282 cuboid components. Our trained 1D convolutional neural network model shows high performance (R2 score of 0.90) for the prediction of local porosity in discrete sub-volumes with dimensions of (700 x 700 x 40) μm³.
It could be demonstrated that the regressor correctly predicts layer-wise porosity changes but presumably has limited capability to predict differences in local porosity. Furthermore, there is a need to study the significance of the used thermogram feature inputs to streamline the model and to adjust the monitoring hardware. Moreover, we identified multiple sources of data uncertainty resulting from the in-situ monitoring setup, the registration with the ground truth X-ray-computed tomography data and the used pre-processing workflow that might influence the model’s performance detrimentally.
Residual stresses Analysis in Additively Manufactured alloys using neutron diffraction (L-PBF)
(2023)
An overview of recent progress at BAM of residual stress analysis in additively manufactured, in particular Laser Powder Bed Fusion of metallics materials, using neutron diffraction will be presented. This will cover important topics of the stress-free reference, the diffraction elastic moduli and principal stress determination.
The European Commission has identified Advanced Manufacturing and Advanced Materials as two of six Key Enabling Technologies (KETs). It is considered that Metrology is a key enabler for the advancement of these KETs. Consequently, EURAMET, the association of metrology institutes in Europe, has strengthened the role of Metrology for these KETs by enabling the creation of a European Metrology Network (EMN) for Advanced Manufacturing. The EMN is comprised of National Metrology Institutes (NMIs) and Designated Institutes (DIs) from across Europe and was formally established in October 2021. The aim of the EMN is to provide a high-level coordination of European metrology activities for the Advanced Manufacturing community.
The EMN itself is organized in three sections representing the major stages of the manufacturing chain: 1) Advanced Materials, 2) Smart Manufacturing Systems, and 3) Manufactured Components & Products. The EMN for Advanced Manufacturing is engaging with stakeholders in the field of Advanced Manufacturing (large companies & SMEs, industry organisations, existing networks, and academia), as well as the wider Metrology community, including Technical Committees, to provide input for the Strategic Research Agenda (SRA) on Metrology for Advanced Manufacturing.
This contribution will give an overview about the first version of the SRA prepared by the EMN for Advanced Manufacturing.
Structural alloys are largely employed in key industrial sectors and their demand is predicted to rise rapidly for the next decades. Most of these materials require a large amount of energy for extraction and manufacturing, which causes the emission of greenhouse gases and other pollutants. Therefore, strategies for improving the sustainability of structural metallic alloys are urgently needed.
Additive Manufacturing (AM), in particular Laser Powder Bed Fusion (PBF-LB/M), aims to be a sustainable manufacturing process, as it allows the build-up of complex geometry in near net-shape from 3D models, while minimizing material waste and the energy required for the process and post-process treatments.
Nevertheless, the application of additively manufactured parts in structural safety-relevant applications is still hindered by the poor fatigue performance. The cause of this has been mainly attributed to the presence of manufacturing defects and surface roughness. Therefore, a huge effort has been made to optimize the process parameters and to introduce post-process treatments to minimize the defect content. However, material flaws cannot be fully eliminated, but these can be considered in a damage tolerance framework for the prediction of the fatigue performance of additively manufactured metallic materials, which is essential for part design and qualification.
This work aims at presenting different modelling strategies for the prediction of the fatigue limit of AM metals. Simple empirical models and more complex models based on fatigue short crack propagation are proposed. The investigated material is an AlSi10Mg alloy fabricated by PBF-LB/M and subjected to two different low-temperature heat-treatments (265°C for 1 h and 300°C for 2h). The results show that the models can provide good approximation of the fatigue limits and help in the interpretation of the scatter of fatigue data.
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). Also, two successful research project in collaboration with CAM2, Sweden are presented.
The capability to produce complexly and individually shaped metallic parts is one of the main advantages of the laser powder bed fusion (PBF LB/M) process. Development of material and machine specific process parameters is commonly based on results acquired from small cubic test coupons of about 10 mm edge length. Such cubes are usually used to conduct an optimization of process parameters to produce dense material. The parameters are then taken as the basis for the manufacturing of real part geometries. However, complex geometries go along with complex thermal histories during the manufacturing process, which can significantly differ from thermal conditions prevalent during the production of simply shaped test coupons. This may lead to unexpected and unpredicted local inhomogeneities of the microstructure and defect distribution in the final part and it is a root cause of reservations against the use of additive manufacturing for the production of safety relevant parts. In this study, the influence of changing thermal conditions on the resulting melt pool depth of 316L stainless steel specimens is demonstrated. A variation of thermo-graphically measured intrinsic preheating temperatures was triggered by an alteration of inter layer times and a variation of cross section areas of specimens for three distinct sets of process parameters. Correlations between the preheating temperature, the melt pool depth, and occurring defects were analyzed. The limited expressiveness of the results of small density cubes is revealed throughout the systematic investigation. Finally, a clear recommendation to consider thermal conditions in future process parameter optimizations is given.
In this paper, we demonstrate the value of 1H NMR
relaxometry for studying the hydration of clinker-reduced, more
climate-friendly cementitious binders. The results were obtained
on typical CEM I cements and samples containing two different
reactive agricultural ashes as well as non-reactive biochars as
supplementary cementitious materials. The findings prove that
time-resolved NMR measurements provide valuable additional
information when combined with classical heat flow calorimetry.