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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.
AI-reflectivity is a code based on artificial neural networks trained with simulated reflectivity data that quickly predicts film parameters from experimental X-ray reflectivity curves. This project has a common root with (ML-reflectivity)[https://github.com/schreiber-lab/ML-reflectivity] and evolved in parallel. Both are linked to the following publication:
Fast Fitting of Reflectivity Data of Growing Thin Films Using Neural Networks A. Greco, V. Starostin, C. Karapanagiotis, A. Hinderhofer, A. Gerlach, L. Pithan, S. Liehr, F. Schreiber, S. Kowarik (2019). J. Appl. Cryst.
For an online live demonstration using a pre-trained network have a look at github.
The analysis of the chemical composition of cement-based materials like concrete is a crucial task in civil engineering. Concrete is a multi-phase material made of cement, water, aggregates, additives and admixtures. During the damage assessment of concrete structures, the external ingress of harmful species like chlorine, alkalis or sulfates needs to be determined. In order to provide a fast and precise analysis of concrete the application of LIBS offers unique possibilities as the technique can detect all elements at air atmosphere.
To determine the condition of concrete, the information about the chemical composition, the ingress of harmful species as well as the evaluation of characteristic values like the used cement type or the type of aggregates are needed. This work presents the two-dimensional evaluation of concrete samples using LIBS and multivariate data analysis of atomic and molecular emission. For the evaluation of the main components different classification methods will be applied to identify the heterogeneity of the samples. With respect to their specific phase, different species will be quantified using multivariate regression (PLS) and important figures of merit will be presented. All results will be verified using Standard procedure of wet chemistry (XRF, potentiometric titration and ICP-OES).
The application of a LIBS system is presented. A diode-pumped low energy laser (3 mJ, 1.5 ns, 100 Hz) and a compact NIR spectrometer are used. A scanner allows the two-dimensional element mapping. For the quantitative analysis calibration of the system is carried out with reference samples in a concentration range of chlorine of 0.05 wt.% to 2.5 wt.%. To determine the 2D distribution of harmful elements (Cl, C), concrete cores were drilled, split and analyzed directly. By comparing the chloride ingress and the carbonation, the interaction of both processes can be visualized in one measurement that takes less than 10 minutes with a drill core of 50 mm x 100 mm. Results obtained were compared and verified with standard measurements.
The separation of two closely spaced 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 for an improved reconstruction of closely spaced defects. This new technique has been studied using a 1D laser array with randomly chosen illumination pattern.
This paper presents the results after applying super resolution algorithms, such as the iterative joint sparsity (IJOSP) algorithm, to our processed measurement data. Different data processing techniques before applying the IJOSP algorithm as well as the influence of regularization parameters in the data processing techniques are discussed. Moreover, the degradation of super resolution reconstruction goodness by the choice of experimental parameters such as laser line width or number of measurements is shown.
The application of the super resolution results in a spatial resolution enhancement of approximately a factor of four which leads to a better separation of two closely spaced defects.
This international symposium is devoted to promote a broad exchange of information on technologies, applications and quality assurance in the area of digital industrial radiology and computed tomography. The meeting provides a unique opportunity for users, scientists, equipment suppliers and all who are interested to discuss the present and future possibilities for industrial applications of the following topics:
- Xray detectors and sources
- Image processing algorithms
- Quantitative imaging
- Defect detection & localisation
- Feature extraction
- Dimensional control
- CT, multi-angle - radiography
- Laminography & Tomosynthesis
- Modelling
- Film replacement
- Phase contrast
- Multi-energies and photon counting
- Other radiation techniques (e.g. - neutrons)
- Multi-techniques fusion
- Standardisation
- Qualification & system reliability
- Inspection for Additive Manufacturing
- Scattering
The revision of ISO 24497:2007 started in 2017 (by decision of Com-V at the IIW annual assembly in Shanghai). After 3 years of discussions and incorporation of many comments from all over the world the working group C-V-E-b headed by Uwe Zscherpel finalized the work with a final draft to be forwarded for final vote at ISO TC 44. The working group C-V-E-b finalised the work and can be resolved. The chair thanks for all the successfully work done, the vote to forward the latest draft to ISO for final vote was done without any negative.