The possibility of using a variety of sensor signals acquired during metal powder bed fusion processes, to support part and process qualification and for the early detection of anomalies and defects, has been continuously attracting an increasing interest. The number of research studies in this field has been characterised by significant growth in the last few years, with several advances and new solutions compared with first seminal works. Moreover, industrial powder bed fusion systems are increasingly equipped with sensors and toolkits for data collection, visualisation and, in some cases, embedded in-process analysis. Many new methods have been proposed and defect detection capabilities have been demonstrated. Nevertheless, several challenges and open issues still need to be tackled to bridge the gap between methods proposed in the literature and actual industrial implementation. This paper presents an updated review of the literature on in-situ sensing, measurement and monitoring for metal powder bed fusion processes, with a classification of methods and a comparison of enabled performances. The study summarises the types and sizes of defects that are practically detectable while the part is being produced and the research areas where additional technological advances are currently needed.

Additive manufacturing (AM) technology is considered one of the most promising manufacturing technologies in the aerospace and defense industries. However, AM components are known to have various internal defects, such as powder agglomeration, balling, porosity, internal cracks and thermal/internal stress, which can significantly affect the quality, mechanical properties and safety of final parts. Therefore, defect inspection methods are important for reducing manufactured defects and improving the surface quality and mechanical properties of AM components. This paper describes defect inspection technologies and their applications in AM processes. The architecture of defects in AM processes is reviewed. Traditional defect detection technology and the surface defect detection methods based on deep learning are summarized, and future aspects are suggested.