Pure copper is an excellent thermal and electrical conductor, however, attempts to process it with additive manufacturing (AM) technologies have seen various levels of success. While electron beam melting (EBM) has successfully processed pure copper to high densities, laser powder bed fusion (LPBF) has had difficulties achieving the same results without the use of very high power lasers. This requirement has hampered the exploration of using LPBF with pure copper as most machines are equipped with lasers that have low to medium laser power densities. In this work, experiments were conducted to process pure copper with a 200W LPBF machine with a small laser spot diameter resulting in an above average laser power density in order to maximise density and achieve low electrical resistivity. The effects of initial build orientation and post heat treatment were also investigated to explore their influence on electrical resistivity. It was found that despite issues with high porosity, heat treated specimens had a lower electrical resistivity than other common AM materials such as the aluminium alloy AlSi10Mg. By conducting these tests, it was found that despite having approximately double the resistivity of commercially pure copper, the resistivity was sufficiently low enough to demonstrate the potential to use AM to process copper suitable for electrical applications. Highlights• Medium powered LPBF machines can process pure Cu to an acceptable level.• Resistivity of as-built Cu increases by 33% depending on initial build orientation.• Resistivity can be reduced by over 50% from as-built conditions via heat treatment.• Electrical resistivity values once heat treated are lower than AlSi10Mg values.

Additive manufacturing (AM) opens up a design freedom beyond the limits of traditional manufacturing techniques. Electrical windings created through AM could lead to more powerful and compact electric motors, but only if the electrical properties of the AM printed part can be shown to be similar to conventionally manufactured systems. Until now, no study has reported on the suitability of AM parts for electrical applications as there are few appropriate materials available to AM for this purpose. AlSi10Mg is a relatively good electrical conductor that does not have the same reported issues associated with processing pure aluminium or copper via selective laser melting (SLM). Here, experiments were conducted to test the effects of geometry and heat treatments on the resistivity of AlSi10Mg processed by SLM. It was found that post heat treatments resulted in a resistivity that was 33% lower than the as-built material. The heat treatment also eliminated variance in the resistivity of as-built parts due to initial build orientation. By conducting these tests, it was found that, with this material, there is no penalty in terms of higher resistivity for using AM in electrical applications, thus allowing more design freedom in future electrical applications.

Purpose Metal-based additive manufacturing is a relatively new technology used to fabricate metal objects within an entirely digital workflow. However, only a small number of different metals are proven for this process. This is partly due to the need to find a new set of parameters which can be used to successfully build an object for every new alloy investigated. There are dozens of variables which contribute to a successful set of parameters and process parameter optimisation is currently a manual process which relies on human judgement. Design/methodology/approach Here, the authors demonstrate the application of machine learning as an alternative method to determine this set of process parameters, the subject of this test is the processing of pure copper in a laser powder bed fusion printer. Data in the form of optical images were collected over the course of traditional parameter optimisation. These images were segmented and fed into a convolutional autoencoder and then clustered to find the clusters which best represented a high-quality result. The clusters were manually scored according to their quality and the results applied to the original set of parameters. Findings It was found that the machine-learned clustering and subsequent scoring reflected many of the observations which were found in the traditional parameter optimisation process. Originality/value This exercise, as well as demonstrating the effectiveness of the ML approach, indicates an opportunity to fully automate the approach to process optimisation by applying labels to the data, hence, an approach that could also potentially be suited for on-the-fly process optimisation. Graphical abstract