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Additive manufacturing (AM) has revolutionised the manufacturing world due to its unique advantages, such as the ability to create complex geometries, work with dissimilar metallic materials, eliminate the need for molds or fixed tooling, and provide economic benefits. However, due to the high complexity and dynamics of AM processes, AM parts are prone to various defects that may affect their mechanical properties and safety. Therefore, the as-built quality cannot meet some strict functional requirements in nuclear, energy and aerospace applications. Non-destructive testing (NDT) techniques have proven to be very effective in inspecting damage, aiding in process optimisation and quality control, which can contribute to enhancing the mechanical properties of AM parts. This work presents a comprehensive and up-to-date review and analysis of NDT techniques for damage detection and in-situ process monitoring in metal-based AM. The major characteristics of NDT techniques are analysed, and the most relevant works and primary challenges that every technique faces are highlighted. Moreover, this paper presents the detection and characterisation of defects based on machine learning combined with different NDT techniques.

Non-destructive testing of metal-based additively manufactured parts and processes: a review