Investigation of neural network algorithm for in-situ X-ray tomography

X-ray tomography is an important non-destructive technique for materials structure analysis. In certain applications, especially during in-situ experiments, the constraints posed by the experimental conditions limit the image quality obtainable from the limited data acquired. Commonly used direct image reconstruction algorithms tend to produce images with insufficient accuracy when fed with limited data, while the more accurate iterative algorithms introduce the challenge of high computational cost. A proposed alternative is the use of machine learning to improve the image quality of direct algorithms. The Mixed-Scale Dense convolutional neural network algorithm (M-SDNet) was therefore utilized in this study to quantitatively investigate its effect in improving the image quality of image reconstructions using direct algorithms, for in-situ tomography. Results are shown for the effect of number of projections, threshold values, and resolution, for data acquired in laboratory conditions. The cavities present in the studied sample were the focus of the quantitative analysis, where parameters like number of cavities, sphericity, and volume fraction were tracked across the output images from using the M-SDNet algorithm. Two different training strategies of M-SDNet; segmentation training and regression training, were compared with the segmentation training proving to better at reproducing cavities in the output images. The reduction on the number of projections and the required scan time suggest that the Mixed-Scale Dense networks are able to significantly improve the accuracy of image reconstructions, and thus suitable to overcome the experimental constraints during in-situ tomograph

Study of the evolution of transport properties induced by additive processing sand mold using X-ray computed tomography

Accurate characterization of the mass transport properties of additively processed sand molds is essential in order to achieve reproducibility of the produced castings and control of gas defects in foundry industries. The present work highlights the potential use of X-ray micro-computed tomography (μ-CT) to characterize the evolution of permeability and some major microstructural features of such additively processed sand molds. The evolution of mass transport properties of sand mold samples under specific processing conditions met in additive manufacturing and its influence on the porosity, the permeability, the tortuosity, and the pore and throat size distributions were characterized from 3D images provided by X-Ray μ-CT. The obtained results showed that the mass transport properties of additively processed sand molds can be closely predicted by using non-destructive in situ methods, such that improvements to the casting can be made to create more optimized 3D printed structures for foundry applications

Pore morphology of polar firn around closure revealed by X-ray tomography

Understanding the slow densification process of polar firn into ice is essential in order to constrain the age difference between the ice matrix and entrapped gases. The progressive microstructure evolution of the firn column with depth leads to pore closure and gas entrapment. Air transport models in the firn usually include a closed porosity profile based on available data. Pycnometry or melting–refreezing techniques have been used to obtain the ratio of closed to total porosity and air content in closed pores, respectively. X-ray-computed tomography is complementary to these methods, as it enables one to obtain the full pore network in 3-D. This study takes advantage of this nondestructive technique to discuss the morphological evolution of pores on four different Antarctic sites. The computation of refined geometrical parameters for the very cold polar sites Dome C and Lock In (the two Antarctic plateau sites studied here) provides new information that could be used in further studies. The comparison of these two sites shows a more tortuous pore network at Lock In than at Dome C, which should result in older gas ages in deep firn at Lock In. A comprehensive estimation of the different errors related to X-ray tomography and to the sample variability has been performed. The procedure described here may be used as a guideline for further experimental characterization of firn samples. We show that the closed-to-total porosity ratio, which is classically used for the detection of pore closure, is strongly affected by the sample size, the image reconstruction, and spatial heterogeneities. In this work, we introduce an alternative parameter, the connectivity index, which is practically independent of sample size and image acquisition conditions, and that accurately predicts the close-off depth and density. Its strength also lies in its simple computation, without any assumption of the pore status (open or close). The close-off prediction is obtained for Dome C and Lock In, without any further numerical simulations on images (e.g., by permeability or diffusivity calculations).</p

Understanding the Interdependence of Penetration Depth and Deformation on Nanoindentation of Nanoporous Silver

International audienceA silver-based nanoporous material was produced by dealloying (selective chemical etching) of an Ag 38.75 Cu 38.75 Si 22.5 crystalline alloy. Composed of connected ligaments, this material was imaged using a scanning electron microscope (SEM) and focused ion-beam (FIB) scanning electron microscope tomography. Its mechanical behavior was evaluated using nanoindentation and found to be heterogeneous, with density variation over a length scale of a few tens of nanometers, similar to the indent size. This technique proved relevant to the investigation of a material's mechanical strength, as well as to how its behavior related to the material's microstructure. The hardness is recorded as a function of the indent depth and a phenomenological description based on strain gradient and densification kinetic was proposed to describe the resultant depth dependence

Investigating performance variations of an optimized GPU-ported granulometry algorithm

International audienceIn this article, we present an optimized GPU implementation of a granulometry algorithm which is used a lot in the study of material domain. The main contribution to this algorithm is the binarization of the input data which increases throughput while reducing data allocated memory space. Also, the optimized GPU implementation brings an order of magnitude speedup compared to a CPU multi-threaded implementation. Furthermore, we investigate the reasons why GPU performance drop for different input data dimensions. Three main factors are exposed: under-exploited threads, threadblocks and streaming multiprocessors. This study should help the reader understand the tight relation that exists between the CUDA programming paradigm and the gpu architecture as well as some main bottlenecks

Recycled Materials in Civil and Environmental Engineering

The aim of this reprint was to report recent innovative studies based on the use of secondary raw materials for applications in civil and environmental engineering. To this purpose, papers were related to the preparation of innovative construction materials and to the treatment of wastes for environmental applications. The investigations were characterized by a common purpose, i.e., to find a way to reduce the amount of waste generated, thus reducing the need for landfilling and optimizing the values of these novel materials, which are an abundant resource that can be easily reused for different applications

Proceedings of the XXIIIrd TELEMAC-MASCARET User Conference 2016, 11 to 13 October 2016, Paris, France

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