Tomography of Materials and Structures最新文献

Limited-angle computed tomography is often imposed by in-situ experiments combining tomography with sample environments. The missing projection data causes artifacts in the tomographic reconstruction. We demonstrate that the correction of these numerical artifacts can be achieved by restoring the missing projections using an iterative reconstruction scheme. The reconstruction is regularized using segmentation, and thresholds determined from the histogram of reconstructed gray levels. The missing projections are simulated by forward projection and incorporated into the original measured dataset to give a complete angular span. This scheme typically converges within a few iterations. Results are presented for several measurements using parallel-beam synchrotron X-ray tomography and 165 degrees of valid projection data. A simple numerical simulation is used to verify the validity of the experimental results.

The mechanical response of laser welds in complex load states can be highly variable, underlying the need for models that can accurately predict mechanical behavior to ensure component performance. In Part I of this work, a series of partial penetration welds of 304L stainless steel have been characterized in three dimensions using micro-computed tomography (μCT). The effect of segmentation approaches for handling raw three-dimensional data has been studied in detail. Such characterization enables for comprehensive analysis of the physical distribution and shape of porosity within the weld as well as details on the geometry of the joint, which are used in conjunction with mechanical testing to understand the impact of these factors on weld performance. Joint geometry, in particular the prescribed gap between the plates, has a large impact on the tensile response of weldments, which can be understood to primarily depend on the local load state that develops around the joint. Using high-fidelity three-dimensional data, the mechanical response of individual weldments, including the peak load and displacement to failure, can be accurately predicted using finite element simulations. The details of the modelling approach, and its sensitivity to various idealizations, are the focus of Part II of this work.

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 straightforward. 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.

Scanning electron microscopy (SEM) is a powerful and versatile technique for materials characterization and present in many laboratories. The integration of an X-ray target holder and detector allows expanding the modalities of SEM by X-ray imaging. These little hardware adaptations enable radiography or X-ray computed tomography (CT) to gain three-dimensional (3D) information about a sample to be investigated. Since SEM-based CT is a non-destructive technique, the method can also image time-dependent changes in microstructure. Presented is the ability of SEM-based nano-CT to image the microstructural evolution of an aluminum-germanium (AlGe32) alloy as a result of annealing. First, the non-destructive CT method is used for an overview scan to identify a hidden region of interest (ROI) in the sample volume at low resolution. The following FIB target preparation reveals the microstructure, which is stepwise annealed and investigated with SEM-based nano-CT at high resolution afterwards. The resulting reconstructed volumes gained from the laboratory-based system are visualized in 3D and show the morphology changes of microstructure. Quantitative analysis reveals grain coarsening and the formation of precipitations in the size of 300–1000 nm. These time-dependent processes are additionally correlated with hardness measurements of the Al alloy.

The mechanical behavior of partial-penetration laser welds exhibits significant variability in engineering quantities such as strength and apparent ductility. Understanding the root cause of this variability is important when using such welds in engineering designs. In Part II of this work, we develop finite element simulations with geometry derived from micro-computed tomography (μCT) scans of partial-penetration 304L stainless steel laser welds that were analyzed in Part I. We use these models to study the effects of the welds’ small-scale geometry, including porosity and weld depth variability, on the structural performance metrics of weld ductility and strength under quasi-static tensile loading. We show that this small-scale geometry is the primary cause of the observed variability for these mechanical response quantities. Additionally, we explore the sensitivity of model results to the conversion of the μCT data to discretized model geometry using different segmentation algorithms, and to the effect of small-scale geometry simplifications for pore shape and weld root texture. The modeling approach outlined and results of this work may be applicable to other material systems with small-scale geometric features and defects, such as additively manufactured materials.

We present a recipe for building a portable DIY toolkit, entitled Kitchen-Based Light Tomography (KBLT) for performing tomography using visible light with low-cost and easily accessible components. We also present different use cases to mimic different challenges in tomography, such as imaging time evolving samples. All the software for motor controls, image acquisition, image reconstruction and analysis is open-sourced and available online. The fast acquisition of KBLT datasets permits 4D scanning (3D plus time), also in combination with so-called sample environments, which can support the advancement of improved image reconstruction algorithms. We believe this ‘Do it yourself’ (DIY) toolkit will be useful to tomography users, beamline scientists and computational researchers, and the tomography community in general.

Misalignment of the rotation axis causes severe artifacts in X-ray computed tomography. Calibration of this parameter is often insufficient for sub-micron resolution measurements and needs to be corrected during the post-processing. This correction can be accelerated by various automatic methods. These vary in mechanisms and performance, making them suitable for different use-cases. This work summarizes existing automatic methods for estimating the rotation axis in X-ray computed tomography, with a focus on sub-micron applications. Some of the methods are implemented and compared in the context of a laboratory sub-micron scanner to demonstrate practical considerations of this task.

The acquisition of high-fidelity 3D grain maps is essential for advancing our understanding of the micromechanical behavior of polycrystalline materials. Grain orientations, grain boundary misorientations, and grain shapes play a significant role in slip transfer mechanisms and grain growth phenomena. The past few years have seen considerable advances in the acquisition of high-reliability grain maps using laboratory-based Diffraction Contrast Tomography (LabDCT). Additionally, the microstructures of challenging sample geometries have become more accessible at the lab scale with recent developments in advanced Lab DCT acquisition strategies, such as helical phyllotaxis Lab DCT (HP-DCT). Unlike a conventional Lab DCT (C-DCT) scan, in which an elongated sample is scanned in multiple sections, a helical phyllotaxis motion is employed in an HP-DCT scan to illuminate the different parts of the sample in a single scan. This strategy can theoretically allow to scan and reconstruct challenging sample geometries, such as elongated and high aspect ratio samples, in a single process with fewer diffraction projections and reduced scan and analyses times. In this study, a detailed analysis of the grain maps for a pure Ti sample obtained from C-DCT and HP-DCT scan data is carried out. The DCT grain maps are compared with the surface grain maps obtained from ground-truth EBSD and SEM scans. Furthermore, the quality of grain reconstructions, grain orientations, grain boundary misorientations, grain shapes and morphology is quantitatively assessed, and the differences in accuracy of grain maps obtained from the conventional and helical phyllotaxis scans are highlighted. These results indicate that the grain reconstructions from HP-DCT scans have comparable grain fidelity to those obtained from C-DCT scan, with the conventional scans performing marginally better in terms of grain shape and orientation but at a higher time cost.