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Data-Driven Modeling for Additive Manufacturing of Metals Proceedings of a Workshop (2019) / Chapter Skim
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3 Microstructure Evolution, Alloy Design, and Part Suitability
Pages 19-27

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From page 19...
... gave opening presentations and were joined by Annett Seide (MTU Aero Engines) , Eric Jägle (Max Planck Institute)
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From page 20...
... During a later m discussion period, a participant asked Levine why there were so few valid submissions. Levine responded that the relationship between residual stress measurements and part distortion models posed challenges.
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From page 21...
... strains • Power distribution function • Part geometry • Local cooling rates Laser tracks and build • Distortion • Etc. layers • Mechanical properties • Widths, cross sections • Fatigue properties Part characterization • Grain shapes, • Corrosion properties • Dislocation density orientations • Etc.
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From page 22...
... With 21 participant teams, the Third Sandia Fracture Challenge centered on predicting tensile failure of an AM part. 2 For more information about Sandia National Laboratories' Stochastic Parallel PARticle Kinetic Simulator, see https://spparks.sandia.gov, accessed October 26, 2018.
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From page 23...
... For thermal process modeling coupled with residual stress prediction, residual stress is still difficult to measure, typeII residual stress is difficult to predict, and an optimization for residual stress is needed. Fast performance prediction accounting for as-built states, properties, and defects for qualification still has to include uncertainty quantification for these materials.
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From page 24...
... In particular, since the microstructure cannot be truly predicted, could a blind prediction be used as the next step? Levine responded that AM-Bench 2018 did ask what phases develop in Inconel 625 during a residual stress heat treatment.
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From page 25...
... She noted that more work is needed to improve modeling capabilities and estimates of relevant material properties; Jägle added that these advances could help researchers achieve desired microstructures and better understand performance. Once there is a microstructure, the models used to translate the microstructure into thermomechanical properties are similar, with additional considerations such as defects that are not present in other materials.
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From page 26...
... . • Long-term goals -- Combining digital volume correlation with machine learning to minimize failure (Johnson)
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From page 27...
... 2017. Impact of heat treatment on mechanical behaviour of Inconel 718 with tailored micro structure processed by selective laser melting.
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Key Terms

  • residual stress
  • alloy design
  • heat treatment
  • sandia national
  • additive manufacturing

  • microstructure prediction
  • thermal process
  • process modeling
  • machine learning
  • modeling coupled

  • lyle levine
  • panel discussion
  • surface tension
  • benchmark measurements
  • participant asked

  • stress heat
  • model guidance
  • solidification process
  • stress measurements
  • model inputs

  • stress prediction
  • heat treatments
  • model validation
  • prediction accounting
  • cooling rates

  • fast performance
  • concurrent multiscale
  • parallel particle
  • engineered net
  • dislocation density


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