The utilization of additive manufacturing presents a significant opportunity for the design of intricate parts that were previously not realizable. However, to successfully manufacture parts using additive manufacturing processes, such as direct energy deposition or powder-bed fusion, certain challenges must be overcome. One potential solution to reduce development times is the adoption of the non-destructive evaluation (NDE) approach, which follows a precise sequence of process steps. This chapter provides an overview of the steps (1) to (4), encompassing all the principal aspects. It is emphasized that the stages within the NDE chain must be considered collectively, and that it may be necessary to revisit previous stages to modify the component or process in question. All the gained data should be processed by an artificial intelligence algorithm in order to optimize the NDE process as well as to better predict the behavior of the parts.

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NDE for Additive Manufacturing

  • Julius Hendl,
  • Axel Marquardt,
  • Alex Selbmann,
  • Anne-Katrin Leopold,
  • Lukas Stepien,
  • Moritz Greifzu,
  • Elena Lopez,
  • Frank Brueckner,
  • Christoph Leyens

摘要

The utilization of additive manufacturing presents a significant opportunity for the design of intricate parts that were previously not realizable. However, to successfully manufacture parts using additive manufacturing processes, such as direct energy deposition or powder-bed fusion, certain challenges must be overcome. One potential solution to reduce development times is the adoption of the non-destructive evaluation (NDE) approach, which follows a precise sequence of process steps. This chapter provides an overview of the steps (1) to (4), encompassing all the principal aspects. It is emphasized that the stages within the NDE chain must be considered collectively, and that it may be necessary to revisit previous stages to modify the component or process in question. All the gained data should be processed by an artificial intelligence algorithm in order to optimize the NDE process as well as to better predict the behavior of the parts.