• :

    Various metals-based additive manufacturing (AM) systems are currently being explored for potential applications in aerospace hardware.  This advanced manufacturing technology presents significant opportunities for accelerated development, agile manufacturing, and design freedom, significantly reducing the process footprint in time and space and enabling rapid prototyping.  A current limitation is the lack of well-defined methodologies for qualifying AM components through non-destructive inspection (NDI), a requirement for production applications.  This multi-faceted problem has the following key aspects:

    1. Flaw definition, identification, and detection (both post-build and in-process)
    2. Flaw detection method verification and validation, and
    3. Flaw remediation, mitigation, and prevention.

    Specifics will vary by AM machine types and material; however, a robust framework based on sound statistical methods is needed that will establish standards and provide guidance to the industry community. The Metals Affordability Initiative (MAI) NG-7 program “Material State Awareness for Additive Manufacturing” (MSAAM) demonstrated the ability to bring together information from new data heavy inspection and characterization methods, using computational and image processing tools to register (align) multiple representations against the intended design and fuse the co-located data together for purposes of analysis and the development of prediction models.  Several test cases from powder bed fusion AM systems will be discussed comparing post-build computed tomography (CT) scans of parts built under different conditions to output from in process monitoring (IPM) sensor systems, implemented using the open-source tool suite DREAM.3D (Digital Representation Environment for Analysis of Microstructure in 3D).  Challenges and lessons learned will be discussed, as well as statistical enablers used to facilitate the iterative model development and assessment processes and adaptations to ultra-high resolution data.

     

     

  • : Mindy Hotchkiss and Sean P. Donegan
  • : Aerojet Rocketdyne and Air Force Research Laboratory (AFRL/RXCM)
  • : Mindy Hotchkiss and Sean P. Donegan
  • : big_data
  • : intermediate
  • : mindy.hotchkiss@rocket.com
  • : 561-882-5331
Translating Images to Information: Improving Process Evaluation and Control Systems for Additive Manufacturing