Selective Electron Beam Melting (SEBM) is a promising powder bed Additive Manufacturing technique for near-net-shape manufacture of high-value titanium components. However without post-manufacture HIPing the fatigue life of SEBM parts is currently dominated by the presence of porosity. In this study, the size, volume fraction, and spatial distribution of the pores in model samples have been characterised in 3D, using X-ray Computed Tomography, and correlated to the process variables. The average volume fraction of the pores (< 0.2%) was measured to be lower than that usually observed in competing processes, such as selective laser melting, but a strong relationship was found with the different beam strategies used to contour, and infill by hatching, a part section. The majority of pores were found to be small spherical gas pores, concentrated in the infill hatched region; this was attributed to the lower energy density and less focused beam used in the infill strategy allowing less opportunity for gas bubbles to escape the melt pool. Overall, increasing the energy density or focus of the beam was found to correlate strongly to a reduction in the level of gas porosity. Rarer irregular shaped pores were mostly located in the contour region and have been attributed to a lack of fusion between powder particles.

Without post-manufacture HIPing the fatigue life of electron beam melting (EBM) additively manufactured parts is currently dominated by the presence of porosity, exhibiting large amounts of scatter. Here we have shown that the size and location of these defects is crucial in determining the fatigue life of EBM Ti-6Al-4V samples. X-ray computed tomography has been used to characterise all the pores in fatigue samples prior to testing and to follow the initiation and growth of fatigue cracks. This shows that the initiation stage comprises a large fraction of life (>70%). In these samples the initiating defect was often some way from being the largest (merely within the top 35% of large defects). Using various ranking strategies including a range of parameters, we found that when the proximity to the surface and the pore aspect ratio were included the actual initiating defect was within the top 3% of defects ranked most harmful. This lays the basis for considering how the deposition parameters can be optimised to ensure that the distribution of pores is tailored to the distribution of applied stresses in additively manufactured parts to maximise the fatigue life for a given loading cycle.

Ti-6Al-4V parts, produced by selective electron beam melting additive manufacturing, have been studied by X-ray computed tomography (XCT) to track pore closure during a standard hot isostatic pressing (HIPing) cycle. Comparison of repeated XCT scans before and after HIPing, on worst-case samples with different geometries, confirmed that all internal porosity was shrunk to below the resolution limit of the equipment used (~5 µm) following the HIPing cycle, apart from defects with surface connected ligaments.

The additive manufacturing of metals requires optimisation to find the melting conditions that give the desired material properties. A key aspect of the optimisation is minimising the porosity that forms during the melting process. A corresponding analysis of pores of different types (e.g. lack of fusion or keyholes) is therefore desirable. Knowing that pores form under different thermal conditions allows greater insight into the optimisation process. In this work, two pore classification methods were trialled: unsupervised machine learning and defined limits. These methods were applied to 3D pore data from X-ray computed tomography and 2D pore data from micrographs. Data were collected from multiple alloys (Ti-6Al-4V, Inconel 718, Ti-5553 and Haynes 282). Machine learning was found to be the most useful for 3D pore data and defined limits for the 2D pore data; the latter worked by optimising the limits using energy densities.

Intelligent application of materials with site-specific properties will undoubtedly allow more efficient components and use of resources. Despite such materials being ubiquitous in nature, human engineering structures typically rely upon monolithic alloys with discrete properties. Additive manufacturing, where material is introduced and bonded to components sequentially, is by its very nature a good match for the manufacture of components with changes in property built-in. Here, some of the recent progress in additive manufacturing of material with spatially varied properties is reviewed alongside some of the challenges facing and opportunities arising from the technology