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The mixture of ammonium nitrate (AN) prills and fuel oil (FO), usually called ANFO, is extensively used in the mining industry as a bulk industrial explosive. One of the major performance predictors of ANFO mixtures is the fuel oil retention, which is itself governed by the porosity of the AN prills. Standardised tests routinely used to assess oil retention face several important limitations; the first being the difficulty to cover the wide range of porosity contents and morphologies from different types of ammonium nitrate prills; the second being the inability to evaluate the closed porosity, which is an important factor regarding the sensitivity of the explosive to detonation. In this study, we present how X-ray computed tomography (XCT), and the associated advanced data processing workflow, can be used to fully characterise the structure and morphology of AN prills. We show that structural parameters such as volume fraction of the different phases and morphological parameters such as specific surface area and shape factor can be reliably extracted from the XCT data, and that there is a good agreement with the measured oil retention values. XCT can therefore be employed to non-destructively and accurately evaluate and characterise porosity in ammonium nitrate prills.
The paper presents the results of a series of combined mechanical and in-situ morphological investigations on highstrength strain-hardening cement-based Composites (SHCC). Tension and compression experiments were performed in a CT scanner employing a dedicated mechanical testing rig. The in-situ microtomographic scans enabled correlating the measured specimen response with relevant microstructural features and fracture processes. The microstructural segmentation of SHCC was performed in the framework of Deep Learning and it targeted an accurate segmentation of pores, fibers and aggregates. Besides their accurate volumetric representation, these phases were quantified in terms of content, size and orientation. The fracture processes were monitored at different loading stages and Digital Volume Correlation (DVC) was employed to spatially map the strains and cracks in the specimens loaded in compression. The DVC analysis highlighted the effect of loading conditions, specimen geometry and material heterogeneity at the mesolevel on the strain distribution and fracture localization.
The material and damage behaviour of additively manufactured polyamide 12 under cyclic loading was characterized by cyclic tests and microstructure analysis by using microscopy, X-ray refraction, and computed tomography. The results were used to determine parameters for the viscoplastic material model by Chaboche and a damage model by Gurson-Tvergaard-Needleman. The temperature was monitored during the experiments and the self-heating effect was observed. By including this effect, a higher accuracy could be achieved with the results of mechanical experiments.
Ammonium nitrate (AN) prills are commonly used as an ingredient in industrial explosives and in fertilisers. Conventional techniques (such as BET or mercury intrusion porosimetry) can measure the open porosity and specific surface area of AN prill, but the closed porosity is not obtainable. This work was focused on evaluating X-ray computed tomography (XCT) as a non-destructive technique for the assessment of porosity in AN prills. An advanced data processing workflow was developed so that the segmentation and quantification of the CT data could be performed on the entire 3D volume, yet allowing the measurements (e.g.; volume, area, shape factor…) to be extracted for each individual phase (prill, open porosity, closed porosity) of each individual prill, in order to obtain statistically relevant data. Clear morphological and structural differences were seen and quantified between fertiliser and explosive products. Overall, CT can provide a very wide range of parameters that are not accessible to other techniques, destructive or non-destructive, and thus offers new insights and complementary information.
Ammonium nitrate (AN) prills are commonly used as an ingredient in industrial explosives and in fertilisers. Conventional techniques (such as BET or mercury intrusion porosimetry) can measure the open porosity and specific surface area of AN prill, but the closed porosity is not obtainable. This work was focused on evaluating X-ray computed tomography (XCT) as a non-destructive technique for the assessment of porosity in AN prills. An advanced data processing workflow was developed so that the segmentation and quantification of the CT data could be performed on the entire 3D volume, yet allowing the measurements (e.g.; volume, area, shape factor…) to be extracted for each individual phase (prill, open porosity, closed porosity) of each individual prill, in order to obtain statistically relevant data. Clear morphological and structural differences were seen and quantified between fertiliser and explosive products. Overall, CT can provide a very wide range of parameters that are not accessible to other techniques, destructive or non-destructive, and thus offers new insights and complementary information.
Durch den großflächigen Einsatz der Computertomographie (CT) in unterschiedlichen Industriebereichen steigen auch die Anforderungen an die quantitative Bildanalyse. Subjektive Bildwahrnehmung muss durch objektive Algorithmen ersetzt werden. In diesem Artikel stellt die Bundesanstalt für Materialforschung und -prüfung (BAM), die seit den 1980er Jahren an der Entwicklung der industriellen CT beteiligt ist, anhand ausgewählter Beispiele den aktuellen Stand ihrer Analysemethoden an verschiedenen Anwendungsbeispielen der CT vor.
We show how the combination of the spatial autocorrelation function and permeability calculations, applied to 3D X-ray computed tomography data, can yield quantitative information on the anisotropy of both meso-structure and fluid flow in Diesel Particulate Filter (DPF) materials, such as Cordierite and SiC. It was found that both the degree of anisotropy, and the orientation of the permeability and meso-structure are similar, but not identical. We confirm that the morphological anisotropy of cordierite materials is weak, and clearly influenced by the extrusion process that determines the main direction of anisotropy. Properties of the autocorrelation function are discussed and it is shown why estimating the characteristic length of real meso-structures (grain or ?pore? size) is not possible. Finally, we show that the autocorrelation function applied on grey-level images can give a good estimate of the degree of anisotropy even with limited resolution.
In order to characterise the material and damage behaviour of additively manufactured polyamide 12 (PA12) under quasi-static load and to implement it in a numerical model, experiments under quasi-static load as well as microstructural investigations were carried out. Selective laser sintering (SLS) was used as the manufacturing process. For the classification of the material behaviour, quasi-static cyclic tests with holding times as well as tensile tests were performed. X-ray refraction and computed tomography (CT) were used to investigate the damage behaviour. The Chaboche model, which has already been applied for metallic materials under thermomechanical loading, served as the basis for the selection of the numerical material model. The same procedure was used for the selection of the damage model, where the Gurson–Tvergaard–Needleman (GTN) model was chosen, which was already used for porous metallic materials. The Chaboche model shows very good agreement with experimental results. Furthermore, the coupling with the GTN model allows a very good modelling of the damage behaviour. Finally, it could be shown that the selected models are suitable to simulate the material and damage behaviour of 3D printed PA12.
The characterisation of AM structures is an important aspect of the AM process, required in order to:
1. optimise the AM printing process
2. assess the quality of produced parts
A wide range of characterisation techniques are available, and the selection can be complex, based on multiple factors.
One output from the MetAMMi project is a good practice guide on the correct choice of characterisation technique.
The quantification of experimental and data analysis errors is most of the times jeopardised by the presence of surface roughness, waviness, as well as by typical measurement artefacts of the XCT technique (for instance refraction at sharp edges). One way to at least estimate the influence of those factors on dimensional measurements and on tolerances is to simulate them using Monte-Carlo method. An alternative strategy is to use independent knowledge (e.g. CMM data) to smoothen/calibrate/correct XCT data.