Analytische Chemie
Filtern
Dokumenttyp
- Zeitschriftenartikel (33)
- Vortrag (13)
- Posterpräsentation (4)
- Beitrag zu einem Tagungsband (3)
Schlagworte
- Computed tomography (14)
- Additive manufacturing (10)
- X-ray refraction (7)
- Neutron Diffraction (5)
- Residual stress (5)
- Computed Tomography (4)
- Porosity (4)
- Synchrotron radiation (4)
- X-ray refraction radiography (4)
- Laser powder bed fusion (3)
Organisationseinheit der BAM
- 8 Zerstörungsfreie Prüfung (53) (entfernen)
Paper des Monats
- ja (1)
Eingeladener Vortrag
- nein (13)
Simulation-Assisted Augmentation of Missing Wedge and Region-of-Interest Computed Tomography Data
(2024)
This study reports a strategy to use sophisticated, realistic X-ray Computed Tomography (CT) simulations to reduce Missing Wedge (MW) and Region-of-Interest (RoI) artifacts in FBP (Filtered Back-Projection) reconstructions. A 3D model of the object is used to simulate the projections that include the missing information inside the MW and outside the RoI. Such information augments the experimental projections, thereby drastically improving the reconstruction results. An X-ray CT dataset of a selected object is modified to mimic various degrees of RoI and MW problems. The results are evaluated in comparison to a standard FBP reconstruction of the complete dataset. In all cases, the reconstruction quality is significantly improved. Small inclusions present in the scanned object are better localized and quantified. The proposed method has the potential to improve the results of any CT reconstruction algorithm.
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.
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.
X-ray refraction is analogous to visible light deflection by matter; it occurs at boundaries between different media. The main difference between visible light and X-rays is that in the latter case deflection angles are very small, from a few seconds to a few minutes of arc (i.e., the refraction index n is near to 1). Importantly, deflection of X-rays is also sensitive to the orientation of the object boundaries. These features make X-ray refraction techniques extremely suitable to a) detect defects such as pores and microcracks and quantify their densities in bulk (not too heavy) materials, and b) evaluate porosity and particle properties such as orientation, size, and spatial distribution (by mapping). While X-ray refraction techniques cannot in general image single defects, they can detect objects with size above a few wavelengths of the radiation.
Such techniques, especially at the Synchrotron BESSY II, Berlin, Germany, can be used in-situ, i.e. when the specimen is subjected to temperatures or external loads.
The use of X-ray refraction analysis yields quantitative information, which can be directly input in kinetics, mechanical and damage models.
We hereby show the application of non-destructive X-ray refraction radiography (SXRR, 2D mapping also called topography) to problems in additive manufacturing:
1) Porosity analysis in PBF-LM-Ti64. Through the use of SXRR, we could not only map the (very sparse) porosity distribution between the layers and quantify it, but also classify, and thereby separate, the filled porosity (unmolten powder) from the keyhole and gas pores (Figure 1).
2) In-situ heat treatment of laser powder bed fusion PBF-LM-AlSi10Mg to monitor microstructure and porosity evolution as a function of temperature (Figure 2). By means of SXRR we indirectly observed the initial eutectic Si network break down into larger particles as a function of increasing temperature. We also could detect the thermally induced porosity (TIP). Such changes in the Si-phase morphology upon heating is currently only possible using scanning electron microscopy, but with a much smaller field-of-view. SXRR also allows observing the growth of some individual pores, usually studied via X-ray computed tomography, but again on much smaller fields-of-view.
Our results show the great potential of in-situ SXRR as a tool to gain in-depth knowledge of the defect distribution and the susceptibility of any material to thermally induced damage and/or microstructure evolution over statistically relevant volumes.
High energy X-ray synchrotron diffraction is used to investigate the elastic anisotropy of the nickel-based superalloy IN718 produced by laser powder bed fusion (PBF-LB). This material is characterized by a columnar grain morphology with some crystallographic texture. The material is subjected to elastic loading to determine the diffraction elastic constants (DECs). Furthermore, the single-crystal elastic constants (SCEC) are refined from these experiments using different micromechanical models. The results show that each micromechanical model predicts a specific set of SCEC that well describes the elastic anisotropy of PBF-LB/IN718.
Regardless of the experimental care practiced in acquiring X-ray computed tomography (XCT) data, artifacts might still exist, such as noise and blur. This is typical for fast XCT data acquisitions (e.g., in-situ investigations), or low-dose XCT. Such artifacts can complicate subsequent analysis of the data. Digital filters can moderately cure extensive artifacts. The selection of filter type, intensity, and order of application is not always straight forward. To tackle these problems, a complete sequential multilevel, multi-scale framework: BAM SynthCOND, employing newly designed deep convolutional neural networks (DCNNs), was formulated. Although data conditioning with neural networks is not uncommon, the main complication is that completely artifact-free XCT data for training do not exist. Thus, training data were acquired from an in-house developed library (BAM SynthMAT) capable of generating synthetic XCT material microstructures. Some novel DCNN architectures were introduced (2D/3D ACEnet_Denoise, 2D/3D ACEnet_Deblur) along with the concept of Assertive Contrast Enhancement (ACE) training, which boosts the performance of neural networks trained with continuous loss functions. The proposed methodology accomplished very good generalization from low resemblance synthetic training data. Indeed, denoising, sharpening (deblurring), and even ring artifact removal performance were achieved on experimental post-CT scans of challenging multiphase Al-Si Metal Matrix Composite (MMC) microstructures. The conditioning efficiencies were: 92% for combined denoising/sharpening, 99% for standalone denoising, and 95% for standalone sharpening. The results proved to be independent of the artifact intensity. We believe that the novel concepts and methodology developed in this work can be directly applied on the CT projections prior to reconstruction, or easily be extended to other imaging techniques such as: Microscopy, Neutron Tomography, Ultrasonics, etc.
Although layer-based additive manufacturing methods such as laser powder bed fusion (PBF-LB) offer an immense geometrical freedom in design, they are typically subject to a build-up of internal stress (i.e. thermal stress) during manufacturing. As a consequence, significant residual stress (RS) is retained in the final part as a footprint of these internal stresses. Furthermore, localized melting and solidification inherently induce columnar-type grain growth accompanied by crystallographic texture. Although diffraction-based methods are commonly used to determine the RS distribution in PBF-LB parts, such features pose metrological challenges in their application. In theory, preferred grain orientation invalidates the hypothesis of isotropic material behavior underlying the common methods to determine RS. In this work, more refined methods are employed to determine RS in PBF-LB/M/IN718 prisms, based on crystallographic texture data. In fact, the employment of direction-dependent elastic constants (i.e. stress factors) for the calculation of RS results in insignificant differences from conventional approaches based on the hypothesis of isotropic mechanical properties. It can be concluded that this result is directly linked to the fact that the {311} lattice planes typically used for RS analysis in nickel-based alloys have high multiplicity and less strong texture intensities compared with other lattice planes. It is also found that the length of the laser scan vectors determines the surface RS distribution in prisms prior to their removal from the baseplate. On removal from the baseplate the surface RS considerably relaxes and/or redistributes; a combination of the geometry and the scanning strategy dictates the sub-surface RS distribution.
In Leichtbauanwendungen sind Materialien mit geringen Dichten und hohen Festigkeiten gefragt. Insbesondere Faser-Kunststoff-Verbunde (FKV) erfüllen diese Anforderungen und werden zunehmend für Strukturbauteile eingesetzt. Die Oberflächen moderner Rotorblätter von Windkraftanlagen werden aus FKV gefertigt. Das Versagen dieser Materialien hat massiven Einfluss auf die Einsatzfähigkeit der gesamten Windkraftanlage. Daher ist die Kenntnis des Versagensverhaltens dieser Komponenten unter statischer und zyklischer Belastung von großem Interesse, um die Betriebsbeanspruchung abzuschätzen. Im Idealfall wird die Schadensentwicklung unter Betriebslast mit zerstörungsfreien in-situ-Prüfverfahren ermittelt. Hier berichten wir über die Beobachtung der Rissentwicklung in Carbon-Faserverstärkten Kunststoffen (CFK) unter kontinuierlicher Zugbelastung durch in-situ Synchrotron-Röntgenrefraktionsradiographie. Eine selbst entwickelte elektromechanische Zugprüfmaschine mit einem Kraftbereich bis zu 15 kN wurde in den Strahlengang an der BAMline (BESSY II) integriert. Da in herkömmlichen (Absorptions-) Radiographien Defekte wie Zwischenfaserbrüche oder Faser-Matrix-Enthaftung keinen ausreichenden Kontrast verursachen, wird zur Kontrastanhebung die Röntgenrefraktion benutzt. Hier wird das sogenannte Diffraction Enhanced Imaging (DEI) angewandt, um Primär- und gebrochene Strahlung mit Hilfe eines Analysatorkristalls zu trennen. Diese Technik ermöglicht schnelle Messungen mit einem Gesichtsfeld von einigen Quadratmillimetern (hier: 14 mm × 7.2 mm) und ist ideal für in-situ Untersuchungen. Die Bildgebung und der Zugversuch erfolgen mit einer Bildfrequenz von 0.7 / s und einer Dehnrate von 0.00055 / s . Bei 0°/90° Fasergelegen treten die ersten Zwischenfaserrisse bei 380 MPa (Dehnung 0.8 %) auf. Vor dem Versagen bei ca. 760 MPa (Dehnung 2.0 %) beobachten wir die Ausbildung eines nahezu äquidistanten Rissmusters (Risse in ca. 1 mm Abstand), das sich im geschädigten Zustand über die gesamte Probe erstreckt.
Die Brechung von Röntgenstrahlen (Röntgenrefraktion) an Grenzflächen zwischen Materialien unterschiedlicher Dichte ist analog zur Ablenk-ung von sichtbarem Licht an z.B. Glasoberflächen. Es gibt jedoch zwei wesentliche Unterschiede:
a) konvexe Grenzflächen verursachen Divergenz (d.h. der Brechungsindex n ist kleiner als 1), und
b) die Ablenkungswinkel sind sehr klein, und reichen von einigen Bogensekunden bis zu einigen Bogenminuten (d.h. n ist nahe bei 1);
Wie auch bei sichtbarem Licht ist die Ablenkungsrichtung der Röntgenstrahlen abhängig von der Orientierung der durchstrahlten Grenzfläche. Aufgrund dieser Eigenschaften eignen sich Röntgenrefraktionsmethoden hervorragend für:
a) die Erkennung und Quantifizierung von Defekten wie Poren und Mikrorissen und
b) die Bewertung von Porosität und Partikeleigenschaften wie Orientierung, Größe und räumliche Verteilung.
Wir zeigen die Anwendung der Röntgenrefraktionsradiographie (2,5D Technik) und der -tomographie (3D Technik) für die Untersuchung verschiedener Probleme in der Werkstoffwissenschaft und -technologie:
a) Sintern von SiC-Grünkörpern
b) Porositätsanalyse in Dieselpartikelfiltersilikaten
c) Faser-Matrix-Haftung in Metall- und Polymermatrixverbundwerkstoffen
d) Mikrorissbildung in Glaskeramik.
Wir zeigen, dass der Einsatz von Röntgenrefraktionsmethoden quantitative Ergebnisse liefert, die direkt als Parameter in Werkstoffmodellen verwendet werden können.
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.