This paper reviews recent improvements in additive manufacturing technologies, focusing on those which have the potential to produce and repair metal parts for the aerospace industry. Electron beam melting, selective laser melting and other metal deposition processes, such as wire and arc additive manufacturing, are presently regarded as the best candidates to achieve this challenge. For this purpose, it is crucial that these technologies are well characterised and modelled to predict the resultant microstructure and mechanical properties of the part. This paper presents the state of the art in additive manufacturing and material modelling. While these processes present many advantages to the aerospace industry in comparison with traditional manufacturing processes, airworthiness and air transport safety must be guaranteed. The impact of this regulatory framework on the implementation of additive manufacturing for repair and production of parts for the aerospace industry is presented.

This paper is focused on Qualification Procedures for metal parts manufactured using new Additive Manufacturing (AM) techniques in the aerospace industry. The main aim is to understand the interaction between these technologies and the stringent regulatory framework of this industry in order to develop correct Quality Assurance and Quality Control procedures in accordance with the certification process for the technology and spare parts. These include all the testing and validation necessary to implement them, as well as to maintain their capability throughout their life-cycle, specific procedures to manufacture or repair parts, work-flows and records amongst others. An entire novel Qualification Procedure for Electron Beam Melting (EBM) to reproduce and repair an aerospace part has been developed and it is presented in this paper. These will be part of the future Quality Assurance and Quality Management systems of those aerospace companies that implement AM in their supply chain.

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This paper discusses the applicability of Visibility Algorithms to detect faults in condition monitoring applications. The general purpose of Visibility Algorithms is to transform time series into graphs and study them through the characterization of their associated network. Degradation of a component results in changes to the network. This technique has been applied using a test rig of an aircraft fuel system to show that there is a correlation between the values of key metrics of visibility graphs and the severity of four failure modes. We compare the results of using Horizontal Visibility algorithms against Natural Visibility algorithms. The results also show how the Kullback-Leibler divergence and statistical entropy can be used to produce condition indicators. Experimental results show that there is little dispersion in the values of condition indicators, leading to a low probability of false positives and false negatives.