Beyond Traditional Tomography: X-ray Multi-projection Imaging for Additive Manufacturing

Abstract: The main aim of this bachelor thesis is to push the limits of 3D X-ray imaging by studying the minimum number of projections required to retrieve high-quality reconstructions for additive manufacturing processes. This project first focuses on understanding how X-ray tomography works, by reconstructing additive manufacturing data already obtained (from PSI, TOMCAT beamline) using two standard reconstruction algorithms, GRIDREC and SIRT. Once 3D reconstructions are obtained with state-of-the-art algorithms, a novel deep-learning algorithm that can reduce the constraints regarding the number of projections and the angular range was used, ONIX. From this, the results obtained by the state-of-the-art approaches and those novel ones in terms of the relevant imaging parameters are compared. This comparison also involves checking given settings where traditional reconstruction fails and analyzing the type of results that novel methods yield. After studying how ONIX can work with a low number of projections, an outlook is provided on how deep learning methods can be used in the future development of additive manufacturing, or 3D printing.