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While the problem of the identification of mechanisms of hydrogen assisted damage has and is being thoroughly studied, the quantitative analysis of such damage still lacks suitable tools. In fact, while, for instance, electron microscopy yields excellent characterization, the quantitative analysis of damage requires at the same time large field-of-views and high spatial resolution. Synchrotron X-ray refraction techniques do possess both features. In this work, we show how synchrotron X-ray refraction computed tomography (SXRCT) can quantify damage induced by hydrogen embrittlement in a lean duplex steel, yielding results that overperform even those achievable by synchrotron X-ray absorption computed tomography. As already reported in literature, but this time using a non-destructive technique, we show that the hydrogen charge does not penetrate to the center of tensile specimens. By the comparison between virgin and hydrogen-charged specimens, we deduce that cracks in the specimen bulk are due to the rolling process rather than hydrogen-assisted. We show that (micro)cracks propagate from the surface of tensile specimens to the interior with increasing applied strain, and we deduce that a significant crack propagation can only be observed short before rupture.
Diffraction enhanced imaging (DEI) is an advanced digital radiographic imaging technique employing the refraction of X-rays to contrast internal interfaces. This study aims at qualitatively and quantitatively evaluating images acquired using this technique and to assess how different fitting functions to the typical rocking curves (RCs) influence the quality of images. RCs are obtained for every image pixel. This allows the separate determination of the absorption and the refraction properties of the material in a position-sensitive manner. Comparison of various types of fitting functions reveals that the Pseudo-Voigt (PsdV) function is best suited to fit typical RCs. A robust algorithm was developed in the Python programming language, which reliably extracts the physically meaningful information from each pixel of the image. We demonstrate the potential of the algorithm with two specimens: a silicone gel specimen that has well-defined interfaces, and an additively manufactured polycarbonate specimen.