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The ability to perform non-destructive areal surface analysis of the internal surfaces of additively manufactured (AM) components would be advantageous during product development, process control and product acceptance. Currently industrial X-ray computed tomography (XCT) is the only practical method for imaging the internal surfaces of AM components. A viable method of extracting useable areal surface texture data from XCT scans has now been developed and this paper reports on three measurement and data processing factors affecting the value of areal parameters per ISO 25178-2 generated from XCT volume data using this novel technique. X-ray, Metrology, Additive manufacturing 2.1. Measurement plates Individual rectangular plates, approximately 10 mm x 20 mm, were cut from a Rubert Microsurf 334 (casting) test panel. The casting panel was used as this surface was considered to most closely represent the surface of a powder bed fusion (PBF) metal AM component. The nominal surface Ra values for the plates used for this work were 50 µm and 25 µm as these approximate the as-Contents lists available at SciVerse ScienceDirect

X-ray computed tomography (CT) has recently started to be used for evaluating the surface topography of metal parts produced by additive manufacturing (AM). In particular, CT can overcome the main limitations of contact and optical measuring techniques, as CT enables non-destructive measurements of both internal and difficult-to-access surfaces, including micro-scale re-entrant surface features. This work aims at improving the understanding of CT-based surface topography characterisation, including the use of new generalised surface texture parameters suited for AM surfaces. Experimental investigations are performed on Ti6Al4V reference samples fabricated by powder bed fusion to determine the uncertainty of CT surface topography measurements.

In-situ layerwise imaging in laser powder bed fusion (L-PBF) has been implemented by many system developers to monitor the powder bed homogeneity. Increasing attention has been recently devoted to the possibility of using the same sensing approach to detect also in-plane and out-of-plane geometrical distortions of the part while it is being produced. To this aim, seminal works investigated the suitability of various image segmentation algorithms and assessed the accuracy of layerwise dimensional and geometrical measurements. Nevertheless, there is a lack of automated methods to identify, in-situ and in-process, geometrical defects and out-of-control deviations from the nominal geometry. This study presents a methodology that combines an active contours methodology for image segmentation with a statistical process monitoring approach suitable to deal with complex geometries that change layer by layer. The proposed approach enables a data-driven and automated alarm rule to detect the onset of geometrical distortions during the build by comparing the slice contour reconstruction with the nominal geometry in each layer. Moreover, by coupling edge-based and region-based segmentation techniques, the method is sufficiently robust to be applied to imaging and illumination setups that are already available on industrial L-PBF systems. The effectiveness of the proposed approach was tested on a real case study involving the L-PBF of complex Ti6Al4V parts that exhibited local geometrical distortions.

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