The occurrence of keyhole porosity in selective laser melting (SLM) has so far not been quantified systematically using X-ray computed tomography (X-CT). In this study, keyhole porosity in selective laser molten Ti-6Al-4V grade 23 parts was analyzed using post-process characterizations. Single tracks were produced with optimum, high volumetric energy density and low volumetric energy density parameters on the top of SLM substrate. High volumetric energy density was achieved by either (i) high laser power and optimum scan speed to study the effect of laser power or (ii) optimum laser power and low scan speed to study the effect of scan speed. Keyhole porosity is sensitive to the SLM process parameters. It was observed by using X-CT that a high amount of keyhole porosity was formed below the line scan produced with a high volumetric energy density achieved by optimum laser power and low scan speed; however, a line scan produced with same volumetric energy density but achieved by high laser power and optimum scan speed resulted in less or no voids. The X-CT porosity analysis helped to understand the size, shape, location, and number of keyhole pores that were formed.
X-ray computed tomography (X-CT) plays an important role in non-destructive quality inspection and process evaluation in metal additive manufacturing, as several types of defects such as keyhole and lack of fusion pores can be observed in these 3D images as local changes in material density. Segmentation of these defects often relies on threshold methods applied to the reconstructed attenuation values of the 3D image voxels. However, the segmentation accuracy is affected by unavoidable X-CT reconstruction features such as partial volume effects, voxel noise and imaging artefacts. These effects create false positives, difficulties in threshold value selection and unclear or jagged defect edges. In this paper, we present a new X-CT defect segmentation method based on preprocessing the X-CT image with a 3D total variation denoising method. By comparing the changes in the histogram, threshold selection can be significantly better, and the resulting segmentation is of much higher quality. We derive the optimal algorithm parameter settings and demonstrate robustness for deviating settings. The technique is presented on simulated data sets, compared between low- and high-quality X-CT scans, and evaluated with optical microscopy after destructive tests.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.