Machine Learning to Optimize Additive Manufacturing Parameters for Laser Powder Bed Fusion of Inconel 718

  • Branden KappesEmail author
  • Senthamilaruvi Moorthy
  • Dana Drake
  • Henry Geerlings
  • Aaron Stebner
Conference paper
Part of the The Minerals, Metals & Materials Series book series (MMMS)


Approximately 3600 samples have been printed to characterize the build parameters for laser powder bed fusion (L-PBF) fabrication of Inconel 718. The tested samples connect pore formation to part orientation, part location and the use of recycled powder. These data serve as the basis for development of a Random Forest Network machine learning (ML) model capable of two-way modeling of process–property and process–structure relationships. These results show how common procedural steps in the setup and execution of L-PBF effect porosity, particularly the formation of keyhole and lack of fusion (LOF) defects, and how data collection, processing, and validation can expose even subtle connections between input features and output parameters using a general ML framework.


Additive manufacturing Laser powder bed fusion Inconel Machine learning X-ray computed tomography Porosity Powder morphology 



This work was supported by the Colorado Office of Economic Development and International Trade (COEDIT) and the Department of Defense Office of Economic Adjustment. One author, BK, would like to thank Ms. Elise Mutz for her assistance in creating Fig. 5 and Citrine Informatics for enabling the RFN analysis and Fig. 9.


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Copyright information

© The Minerals, Metals & Materials Society 2018

Authors and Affiliations

  • Branden Kappes
    • 1
    Email author
  • Senthamilaruvi Moorthy
    • 1
  • Dana Drake
    • 1
  • Henry Geerlings
    • 1
  • Aaron Stebner
    • 1
  1. 1.Colorado School of MinesGoldenUSA

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