Wissenschaftliche Artikel der BAM
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- Additive manufacturing (27)
- Computed tomography (13)
- Residual stress (11)
- X-ray computed tomography (10)
- Additive Manufacturing (8)
- Laser powder bed fusion (8)
- Simulation (7)
- Neutron diffraction (6)
- Neutron imaging (5)
- Residual Stress (5)
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The morphology of the antennal hearts in the head of Phasmatodea and Embioptera was investigated with particular reference to phylogenetically relevant key taxa. The antennal circulatory organs of all examined species have the same basic construction: they consist of antennal vessels that are connected to ampullae located in the head near the antenna base. The ampullae are pulsatile due to associated muscles, but the points of attachment differ between the species studied. All examined Phasmatodea species have a Musculus (M.) interampullaris which extends between the two ampullae plus a M. ampulloaorticus that runs from the ampullae to the anterior end of the aorta; upon contraction, all these muscles dilate the lumina of both ampullae at the same time. In Embioptera, only the australembiid Metoligotoma has an M. interampullaris. All other studied webspinners instead have a M. ampullofrontalis which extends between the ampullae and the frontal region of the head capsule; these species do not have M. ampulloaorticus. Outgroup comparison indicates that an antennal heart with a M. interampullaris is the plesiomorphic character state among Embioptera and the likely ground pattern of the taxon Eukinolabia. Antennal hearts with a M. ampullofrontalis represent a derived condition that occurs among insects only in some embiopterans. These findings help to further clarify the controversially discussed internal phylogeny of webspinners by supporting the view that Australembiidae are the sister group of the remaining Embioptera.
Metastable austenitic stainless steel (304L) samples with a rectangular cross-section were plastically deformed in torsion during which they experienced multiaxial stresses that led to a complex martensitic phase distribution owing to the transformation induced plasticity effect. A three-dimensional characterization of the phase distributions in these cm-sized samples was carried out by wavelength-selective neutron tomography. It was found that quantitatively correct results are obtained as long as the samples do not exhibit any considerable preferential grain orientation. Optical microscopy, electron backscatter diffraction, and finite element modeling were used to verify and explain the results obtained by neutron tomography. Altogether, neutron tomography was shown to extend the range of microstructure characterization methods towards the meso- and macroscale.
Additive manufacturing (AM) of metals and in particular laser powder bed fusion (LPBF) enables a degree of freedom in design unparalleled by conventional subtractive methods. To ensure that the designed precision is matched by the produced LPBF parts, a full understanding of the interaction between the laser and the feedstock powder is needed. It has been shown that the laser also melts subjacent layers of material underneath. This effect plays a key role when designing small cavities or overhanging structures, because, in these cases, the material underneath is feed-stock powder. In this study, we quantify the extension of the melt pool during laser illumination of powder layers and the defect spatial distribution in a cylindrical specimen. During the LPBF process, several layers were intentionally not exposed to the laser beam at various locations, while the build process was monitored by thermography and optical tomography. The cylinder was finally scanned by X-ray computed tomography (XCT). To correlate the positions of the unmolten layers in the part, a staircase was manufactured around the cylinder for easier registration. The results show that healing among layers occurs if a scan strategy is applied, where the orientation of the hatches is changed for each subsequent layer. They also show that small pores and surface roughness of solidified material below a thick layer of unmolten material (>200 µm) serve as seeding points for larger voids. The orientation of the first two layers fully exposed after a thick layer of unmolten powder shapes the orientation of these voids, created by a lack of fusion.
The damage mechanisms of metallic components produced by process laser powder bed fusion differ significantly from those typically observed in conventionally manufactured variants of the same alloy. This is due to the unique microstructures of additively manufactured materials. Herein, the focus is on the study of the evolution of creep damage in stainless steel 316L specimens produced by laser powder bed fusion. X-ray computed tomography is used to unravel the influence of the process-specific microstructure from the influence of the initial void distribution on creep damage mechanisms. The void distribution of two specimens tested at 600 °C and 650 °C is analyzed before a creep test, after an interruption, and after fracture. The results indicate that the formation of damage is not connected to the initial void distribution. Instead, damage accumulation at grain boundaries resulting from intergranular cracking is observed.
Rapid cooling rates and steep temperature gradients are characteristic of additively manufactured parts and important factors for the residual stress formation. This study examined the influence of heat accumulation on the distribution of residual stress in two prisms produced by Laser Powder Bed Fusion (LPBF) of austenitic stainless steel 316L. The layers of the prisms were exposed using two different border fill scan strategies: one scanned from the centre to the perimeter and the other from the perimeter to the centre. The goal was to reveal the effect of different heat inputs on samples featuring the same solidification shrinkage. Residual stress was characterised in one plane perpendicular to the building direction at the mid height using Neutron and Lab X-ray diffraction. Thermography data obtained during the build process were analysed in order to correlate the cooling rates and apparent surface temperatures with the residual stress results. Optical microscopy and micro computed tomography were used to correlate defect populations with the residual stress distribution. The two scanning strategies led to residual stress distributions that were typical for additively manufactured components: compressive stresses in the bulk and tensile stresses at the surface. However, due to the different heat accumulation, the maximum residual stress levels differed. We concluded that solidification shrinkage plays a major role in determining the shape of the residual stress distribution, while the temperature gradient mechanism appears to determine the magnitude of peak residual stresses.
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 greatest challenge when using deep convolutional neural networks (DCNNs) for automatic segmentation of microstructural X-ray computed tomography (XCT) data is the acquisition of sufficient and relevant data to train the working network. Traditionally, these have been attained by manually annotating a few slices for 2D DCNNs. However, complex multiphase microstructures would presumably be better segmented with 3D networks. However, manual segmentation labeling for 3D problems is prohibitive. In this work, we introduce a method for generating synthetic XCT data for a challenging six-phase Al–Si alloy composite reinforced with ceramic fibers and particles. Moreover, we propose certain data augmentations (brightness, contrast, noise, and blur), a special in-house designed deep convolutional neural network (Triple UNet), and a multi-view forwarding strategy to promote generalized learning from synthetic data and therefore achieve successful segmentations. We obtain an overall Dice score of 0.77. Lastly, we prove the detrimental effects of artifacts in the XCT data on achieving accurate segmentations when synthetic data are employed for training the DCNNs. The methods presented in this work are applicable to other materials and imaging techniques as well. Successful segmentation coupled with neural networks trained with synthetic data will accelerate scientific output.
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.
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.
Sandwich-Schalenstrukturen in situ charakterisiert mit Thermographie und Felddehnungsmessungen
(2018)
An der BAM wurde ein Schalenprüfstand entwickelt, mit dem typische Sandwichschalenstrukturen unter simulierter Betriebsbeanspruchung belastet werden können. Im mechanischen Ermüdungsversuch wurden in situ die Entstehung von Schäden in Schalenprüfkörpern mit Test-Imperfektionen mittels der passiven Thermographie und Felddehnungsmessungen verfolgt. Damit konnten der Zeitpunkt, der Schadensursprung und der Schadensfortschritt zerstörungsfrei bestimmt werden. Zudem wurden vergleichend Schalenprüfkörper mit Luftultraschall und der Röntgen-Laminographie untersucht. Im Rahmen eines BMWi-Forschungsvorhabens wurden die Schädigungsmechanismen evaluiert und Empfehlungen für die Praxis der Fertigung von Rotorblättern abgeleitet. In diesem Beitrag wird sich auf die Darstellung der ZfP-Verfahren und der ZfP-Ergebnisse konzentriert.