Areal surface texture measurement of additively manufactured parts

Surface finish has a significant effect on the fatigue resistance of metal medically implanted devices. Local surface porosity defects, more prevalent with components manufactured using additive techniques than with those manufactured from bar stock of forgings, act as stress raisers reducing strength and fatigue life.
Surface topography is three-dimensional in nature and so industry-standard profile measurements will give an incomplete characterisation of the real surface topography and local surface porosity may not be recognised. The complex nature of the surfaces of additively manufactured (AM) components lend themselves to analysis using areal field and feature parameters, surface segmentation and defect analysis.
One significant advantage of AM over conventional manufacturing techniques, such as milling and turning, is the ability to manufacture complex internal features. Non-destructive direct measurement of the surfaces of the features, using stylus or optical instruments for example, may not be possible. One non-destructive method to image AM components, including internal features, is x-ray computed tomography (CT).
This presentation reports on the novel generation of surface maps from CT volume data and from these maps the generation of areal parameter data per ISO 25178-2. The data generated is compared to data for the same surface areas when measured on an optical focus variation instrument. CT surface texture measurement capability is discussed. Additional testing has been performed to ascertain the sensitivity of a series of areal parameters to the surface changes during AM component post-processing. Two AM selective laser melting titanium Ti6Al4V test components were processed from the as-manufactured condition using vibro-polishing and bead blasting. The surface texture of the components was analysed during the processing and areal parameter data was generated. The sensitivity of selected areal parameters to the surface changes during processing will be presented. The detection of surface defects, such as porosity, will be discussed.