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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.
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.
This study reports the results of the preliminary assessment to fabricate Ti-10at% Nb alloy by electron beam melting (EBM®) from a blend of elemental Nb and Ti powders. The microstructure of the EBM-manufactured Ti-10at% Nb alloys is sensitive to the following factors: different sintering properties of Nb and Ti powders, powder particle properties, material viscosities at varying melt pool temperatures, β-stabilizer element content and the EBM® process parameters. Three phases were observed in as-manufactured Ti-10at% Nb alloy: μm-size Nb phase, a Nb-rich β-solid solution surrounding Nb phase, lamellar structured α-phase and β-solid solution with different distribution and volume fraction. Thus, the combination of powder particle characteristics, very short time material spends in molten condition and sluggish kinetics of mixing and diffusional process in Ti-Nb alloy results in heterogeneous microstructures depending on the local Nb content in the powder blend and the EBM® process conditions.
The performance of grating interferometers coming up now for Imaging interfaces within materials depends on the efficiency (visibility) of their main component, namely the phase grating. Therefore, experiments with monochromatic synchrotron radiation and corresponding simulations are carried out. The visibility of a Phase grating is optimized by different photon energies, varying detector to grating distances and continuous rotation of the phase grating about the grid lines. Such kind of rotation changes the projected grating shapes, and thereby the distribution profiles of phase shifts. This yields higher visibilities than derived from ideal rectangular shapes. By continuous grating rotation and variation of the propagation distance, we achieve 2D visibility maps. Such maps provide the visibility for a certain combination of grating orientation and detector position. Optimum visibilities occur at considerably smaller distances than in the standard setup.
The influence of the process gas, laser scan speed, and sample thickness on the build-up of residual stresses and porosity in Ti-6Al-4V produced by laser powder bed fusion was studied. Pure argon and helium, as well as a mixture of those (30% helium), were employed to establish process atmospheres with a low residual Oxygen content of 100 ppm O2. The results highlight that the subsurface residual stresses measured by X-ray diffraction were significantly lower in the thin samples (220 MPa) than in the cuboid samples (645 MPa). This difference was attributed to the shorter laser vector length, resulting in heat accumulation and thus in-situ stress relief. The addition of helium to the process gas did not introduce additional subsurface residual stresses in the simple geometries, even for the increased scanning speed. Finally, larger deflection was found in the cantilever built under helium (after removal from the baseplate), than in those produced under argon and an argon-helium mixture. This result demonstrates that complex designs involving large scanned areas could be subjected to higher residual stress when manufactured under helium due to the gas’s high thermal conductivity, heat capacity, and thermal diffusivity.
Validation of a fast and traceable radiographic scale calibration of dimensional computed tomography
(2022)
A fast and highly precise method of determining the geometrical scale factor of computed tomography (CT) measurements has been validated successfully by Bundesanstalt für Materialforschung und -prüfung (BAM), the Federal Institute of Metrology (METAS) and Physikalisch-Technische Bundesanstalt (PTB) within the scope of AdvanCT (Advanced Computed Tomography for dimensional and surface measurements in industry), a project funded in the European Metrology Programme for Innovation and Research (EMPIR). The method has been developed by PTB and requires only two radiographic images of a calibrated thin 2D standard (hole grid standard) from two opposite directions. The mean grid distance is determined from both radiographs. From this and with the help of the calibration result, the radiographic scale and therefore the voxel size is determined. The procedure takes only a few minutes and avoids a time-consuming CT scan. To validate the method, the voxel sizes determined via this method were compared with voxel sizes determined from CT scans of calibrated objects. Relative deviations between the voxel sizes in the range of 10−5 were
achieved with minimal effort using cone-beam CT systems at moderate magnifications.
In the present study, in-situ observation of Hot Isostatic Pressure (HIP) procedure of laser powder bed fusion manufactured Ti-6Al-4V parts was performed to quantitatively estimate the densifcation rate of the material and the infuence of the defect initial size and shape on such rate. The observations were performed in-situ using the Ultrafast Tomography Paris-Edinburgh Cell and the combination of fast phase-contrast synchrotron X-ray tomography and energy dispersive difraction. With this strategy, we could quantify how the efectiveness of HIP depends on the characteristics of a defect. Smaller defects showed a higher densifcation rate, while the defect shape did not have signifcant efect on such rate.
The paper is motivated by some inconsistencies and contradictions present in the literature on the calculation of the so-called diffraction elastic constants. In an attempt at unifying the views that the two communities of Materials Science and Mechanics of Materials have on the subject, we revisit and define the terminology used in the field. We also clarify the limitations of the commonly used approaches and Show that a unified methodology is also applicable to textured materials with a nearly arbitrary grain shape. We finally compare the predictions based on this methodology with experimental data obtained by in situ synchrotron radiation diffraction on additively manufactured Ti-6Al4V alloy. We show that (a) the transverse isotropy of the material yields good agreement between the best-fit isotropy approximation (equivalent to the classic Kröner’s model) and the experimental data and (b) the use of a general framework allows the calculation of all components of the tensor of diffraction elastic constants, which are not easily measurable by diffraction methods. This allows us to extend the
current state-of-the-art with a predictive tool.
Connecting Diffraction-Based Strain with Macroscopic Stresses in Laser Powder Bed Fused Ti-6Al-4V
(2020)
The laser powder bed fusion (LPBF) production process often results in large residual stress (RS) in the parts. Nondestructive techniques to determine RS are badly needed. However, a reliable quantification of macro-RS (i.e., stress at the component level) by means of diffraction-based techniques is still a great challenge, because the link between diffraction-based strain and macro-RS is not trivial. In this study, we experimentally determine (by means of in-situ synchrotron radiation diffraction) this link for LPBF Ti-6Al-4V. We compare our results with commonly used models to determine the so-called diffraction elastic constants (DECs). We show that LPBF materials possess different DECs than wrought alloys, simply because their microstructural and mechanical properties are different. We also show that the existing models can be used to calculate DECs only if high accuracy of the RS values is not required. If the peculiarities of the microstructure have to be taken into account (as is the case of additively manufactured materials), a radically new approach is desirable.