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.

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This is a copy of the published version, or version of record, available on the publisher's website. This version does not track changes, errata, or withdrawals on the publisher's site.

No abstract

Fabricating mirrors using additive manufacturing (AM; 3D printing) is a promising yet under-researched production route. There are several issues that need to be better understood before AM can be fully adopted to fabricate mirror substrates. A significant obstacle to AM adoption is the presence of porosity and the influence that has on the resultant optical proprieties. Several batches of high-silicon aluminium (AlSi10Mg) samples were created to investigate the relationships laser parameters, laser paths and build orientations have with the porosity. The results showed that eliminating defects relies on a complex interaction of the process parameters and material properties, with the residual heating from the laser proving to be a significant factor. In addition, the use of a hot isostatic press is investigated and some full prototypes of the Cassegrain CubeSat were produced.