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Data-Driven Modeling for Additive Manufacturing of Metals Proceedings of a Workshop (2019) / Chapter Skim
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5 Product and Process Qualification and Certification
Pages 41-46

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From page 41...
... • What new standards, methods, or techniques need to be devel oped to certify a part built with AM? PROCESS QUALIFICATION AND TECHNOLOGICAL VALIDATION, FROM CASTING TO ADDITIVE Paolo Gennaro, GF Precicast Additive SA Gennaro introduced GF Precicast Additive SA, including its three large divisions: GF Piping Systems, GF Casting Solutions, and GF Machining Solutions.
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From page 42...
... . GF Precicast Additive SA has a fully certified supply chain, including the AM build, heat treatment and hot isostatic pressing, and finishing quality inspection.
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From page 43...
... He also showed a demonstration of a mesoscale model of a track-level simulation. Nonlinear thermomechanical feeds can be determined by inputting data on the laser power, laser efficiency, laser speed, stripe angle, stripe angle increment, hatch distance, and powder material.
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From page 44...
... He confirmed that his team regularly does quantitative validations for thermal and mechanical distortions of the parts and discussed how a spatial grain structure model compared to experimental results. Another audience member commented on the distortion model and the quantitative validation: if the support structure were too complex to model, finding trends in the model data could help avoid the problem.
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From page 45...
... Ricker added that many tests are currently required to assess variability for qualification, and models can help understand variability and build trust in the systems. Teter emphasized the importance of representing the underlying physics of the materials and mechanical properties in machine learning models to increase the meaningfulness of possible predictions.
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From page 46...
... Paquit added that his team stores a large amount of data to help address future questions and that it is important to have diversity in the data. Another participant commented that it is important to learn how to use these data to support decision making; in the future, hopefully all data will be usable for production.
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Key Terms

  • precicast additive
  • paolo gennaro
  • melt pool
  • supply chain
  • management standards

  • gennaro stated
  • help understand
  • teter mentioned
  • stripe angle
  • customer standards

  • digital manufacturing
  • metal laser
  • gennaro discussed
  • technological validation
  • asset management

  • standards exist
  • beam melting
  • international organization
  • participant asked
  • future questions

  • qualification standards
  • team stores
  • overall trust
  • casting solutions
  • increase overall

  • measurement errors
  • john turner
  • majmudar responded
  • understand measurement
  • common causes


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