Imager-Based Techniques for Analyzing Metallic Melt Pools for Additive Manufacturing

  • Cedric Hayes
  • Caleb Schelle
  • Greg Taylor
  • Bridget Martinez
  • Garrett Kenyon
  • Thomas Lienert
  • Yongchao Yang
  • David MascareñasEmail author
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)


Presented is a vision-based algorithm for extracting physical properties from melt pools. The bandwidth requirements for traditional high speed video are too high for real time analysis so silicon retinas are used. This method of imaging has a very fine temporal resolution, high dynamic range, and low bandwidth requirements. The ability to monitor melt pools in real time would improve the quality of laser printed parts and welds because it would allow automatic control systems to recognize and correct imperfections during the printing and welding processes. By measuring the change of intensity within a melt pool then applying blind source separation techniques, spatiotemporal data can be extracted. First a circular membrane model is evaluated to validate the technique. Then the separation technique is performed with a traditional camera on gallium pools of different depths and various lighting conditions. Finally, silicon retina data is used to show that the technique can be applied for this type of imager.


Additive manufacturing Event-based imaging Melt pool depth Blind source separation Modal analysis 



This work was completed as a part of the 2018 Los Alamos National Laboratory Dynamic Summer School. Los Alamos National Laboratory is operated by Los Alamos National Security LLC, for the National Nuclear Security Administration of the U.S. Department of Energy, under DOE Contract DE-AC52-06NA25396.


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Copyright information

© Society for Experimental Mechanics, Inc. 2020

Authors and Affiliations

  • Cedric Hayes
    • 1
  • Caleb Schelle
    • 2
  • Greg Taylor
    • 3
  • Bridget Martinez
    • 4
  • Garrett Kenyon
    • 5
  • Thomas Lienert
    • 6
  • Yongchao Yang
    • 7
  • David Mascareñas
    • 8
    Email author
  1. 1.New Mexico Institute of Mining and TechnologySilver CityUSA
  2. 2.Los Alamos National Laboratory (LANL), MSC 932Los AlamosUSA
  3. 3.New Mexico State UniversityLas CrucesUSA
  4. 4.LANL, Los Alamos National Laboratory MS T001Los AlamosUSA
  5. 5.LANL, Los Alamos National Laboratory MS B256Los AlamosUSA
  6. 6.Los Alamos National LaboratoryLos AlamosUSA
  7. 7.Argonne National LaboratoryLemontUSA
  8. 8.Engineering InstituteLos Alamos National LaboratoryLos AlamosUSA

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