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In this work, synchrotron X-ray refraction radiography (SXRR) was combined with in-situ heat treatment to monitor microstructure and porosity evolution as a function of temperature. The investigated material was a laser powder bed fusion (LPBF) manufactured AlSi10Mg, where the initial eutectic Si network is known to break down into larger particles with increasing temperature. Such alloy is also prone to thermally induced porosity (TIP). We show that SXRR allows detecting the changes in the Si-phase morphology upon heating, while this is currently possible only using scanning electron microscopy. SXRR also allows observing the growth of pores, usually studied via X-ray computed tomography, but on much smaller fields-of-view. Our results show the great potential of in-situ SXRR as a tool to gain in-depth knowledge of the susceptibility of any material to thermally induced damage and/or microstructure evolution over statistically relevant volumes.
Alternative to conventional transmission-based radiography and computed tomography, X-ray refraction techniques are being increasingly used to detect damage in light materials. In fact, their range of application has been recently extended even to metals. The big advantage of X-ray refraction techniques is that they are able to detect nanometric defects, whose size would lie below the resolution of even state-of-the-art synchrotron-based X-ray computed tomography (SXCT). The superiority of synchrotron X-ray refraction radiography and tomography (SXRR and SXRCT) has been shown in the case of light materials, in particular composites. X-ray refraction techniques also yield a quantitifaction of the amount of damage (the so-called relative internal specific surface) and can well be compared with damage models. At the same time, it is impossible for SXRR and SXRCT to image single defects. We show that the combination of refraction- and transmission-based imaging techniques yields an impressive amount of additional information about the type and amount of defects in microstructured materials such as additively manufactured metals or metal matrix composites. We also show that the use of data fusion techniques allows the classification of defects in statistically significant representative volume elements.