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.

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

Marco Grasso;GALLINA, FRANCESCO GIUSEPPE;Bianca Maria Colosimo
2018-01-01

Abstract

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.
2018
Proceedings of the 15th CIRP Conference on Computer Aided Tolerancing
Electron beam melting, data fusion, statistical process monitoring, support vector machine
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1070857
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