One of the main challenges during digital post-processing of x-ray computed tomographic (XCT) measurement data is the reconstruction of the surface geometry of the measured objects. Conventionally, the surface geometry is defined as an isosurface of identical greyscale values, i.e. the x-ray absorbance of the material, based on a linear interpolation between neighboring voxels. Due to the complex surface geometry and rough surface, XCT measurements of additively manufactured (AM) parts are particularly prone to measurement artefacts caused by various physical effects when the x-rays penetrate the material. The irregular greyscale values at the measured surface geometry render commonly used single threshold greyscale value based isosurfaces as insufficient for representing the external and internal surface of the measured objects. This issue becomes particularly apparent when measuring multi-material objects, such as additively manufactured objects with integrated RFID tags. To address this challenge, this study presents a methodology for reliable surface geometry determination of XCT data based on previously acquired fringe projection (FP) data. For this purpose, the conventionally acquired surfaces geometries from the XCT and FP measurements are extracted, pre-processed and registered to each other before being merged into a single mesh. This merged data set is subsequently used as a starting point or reference for a locally adaptive threshold surface detection algorithm, which is able to capture the surface geometry at a sub-voxel resolution. In order to validate the methodology and confirm the envisaged benefits, selected geometry elements of the resulting surface geometry from measurements samples manufactured by additive manufacturing with integrated RIFD tags are compared to coordinate measurement machine (CMM) reference measurements. The results indicate a more robust surface geometry detection against artifacts especially for multi-material applications.

The digital 3D representation of manufactured parts plays a crucial role in the quality assurance of small, individualized lot sizes. Machine vision systems, such as optical 3D scanners, guided by an industrial robotic arm, allow for a contactless full digital reconstruction of surface geometries. In order to digitize the whole geometry, it is necessary to acquire 3D scans from multiple viewpoints with respect to the part, which in combination cover the entire surface. With efficiency in mind, this results in an optimization problem between a high surface area coverage and low measurement effort, referred to as the view planning problem. In the presented work, two popular viewpoint candidate generation methods are implemented: Firstly, a surface-based random sampling method, which generates viewpoints within a solution space, in which visibility of a given model surface can be expected. Secondly, a view sphere viewpoint generation approach, which is independent of the object geometry but avoids clustering by generating evenly spaced viewpoints on a sphere around the centre of the object. Using an adjustable remeshing procedure, a multi-stage approach is implemented by generating multiple meshes with different resolutions. Through this, the benefits of working on a coarse mesh, such as fast viewpoint candidate evaluation and selection, are combined with the level of detail of a fine mesh. It is found that it is possible to considerably reduce the mesh resolution while maintaining a reasonably high surface area coverage on a reference model. Applying the proposed procedure to the view planning for a state-of-the-art 3D fringe light projection scanner with highly sophisticated scanning capabilities, it is demonstrated that the view sphere approach is more suitable for this use-case due to the large measurement volumes of the 3D scanner. The frequently used random sampling approach requires an excessively higher computational effort to achieve similar results in comparison.

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