Data fusion methods for statistical process monitoring and quality characterization in metal additive manufacturing

Metal additive manufacturing (AM) technologies enable the production of complex shapes, lightweight structures and novel functional features. Such increased complexity of the products imposes various challenges in terms of statistical process monitoring and quality assessment. However, one great potential of AM processes, compared to conventional ones, consists of the possibility of gathering a large amount of data layer by layer. This study investigates a data fusion methodology to combine in-situ data from multiple sensors embedded in Electron Beam Melting (EBM) systems to automatically detect faults and process errors. The aim consists of making sense of information already available from the system to enhance its embedded intelligence via novel data mining techniques. A real case study in EBM is presented and discussed.