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Data-Driven Modeling for Additive Manufacturing of Metals: Proceedings of a Workshop (2019)

Chapter: Appendix A: Registered Workshop Participants

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Suggested Citation:"Appendix A: Registered Workshop Participants." National Academies of Sciences, Engineering, and Medicine. 2019. Data-Driven Modeling for Additive Manufacturing of Metals: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25481.
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A

Registered Workshop Participants

Ranadip Acharya, United Technologies Research Center

Jian Cao, Northwestern University

Michele Chiumenti, Uni Barcelona

Bianca Colosimo, Politecnico di Milano

Laurent D’Alvise, GeonX

Michel Delanaye, GeonX

Shawn Dirk, Sandia National Laboratories

Ben Dutton, Manufacturing Technology Centre

Tahany El-Wardany, United Technologies Research Center

Paolo Gennaro, GF Precicast Additive SA

Jarred Heigel, National Institute of Standards and Technology

Eric Jägle, Max Planck Institute

Kyle Johnson, Sandia National Laboratories

Winfried Keiper, European Technology Platform for Advanced Engineering Materials and Technologies

Jonathan King, Department of Energy

Carolin Körner, Friedrich-Alexander Universität Erlangen-Nürnberg

Deniece Korzekwa, Los Alamos National Laboratory

Christian Leinenbach, Empa

Lyle Levine, National Institute of Standards and Technology

Wing Kam Liu, Northwestern University

Adhish Majmudar, GeonX

Ade Makinde, General Electric Global Research Center

Page 60 Cite
Suggested Citation:"Appendix A: Registered Workshop Participants." National Academies of Sciences, Engineering, and Medicine. 2019. Data-Driven Modeling for Additive Manufacturing of Metals: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25481.
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Matthias Markl, Friedrich-Alexander Universität Erlangen-Nürnberg

Mustafa Megahed, ESI Group

Celia Merzbacher, SRI International

Vincent Paquit, Oak Ridge National Laboratory

Janki Patel, National Academies of Sciences, Engineering, and Medicine

Nancy Reid, University of Toronto

Daniel Reznik, Siemens

Richard Ricker, National Institute of Standards and Technology

R. Allen Roach, Sandia National Laboratories

Johannes Schilp, Universität Augsburg Lehrstuhl für Produktionsinformatik

Johannes Henrich Schleifenbaum, Fraunhofer Institute for Laser Technology

Michael Schmidt, Friedrich-Alexander Universität Erlangen-Nürnberg

Michelle Schwalbe, National Academies of Sciences, Engineering, and Medicine

Annett Seide, MTU Aero Engines

Marvin Siewert, University of Bremen

Josh Sugar, Sandia National Laboratories

Amit Surana, United Technologies Research Center

Erik Svedberg, National Academies of Sciences, Engineering, and Medicine

Jens Telgkamp, Airbus Operations GmbH

David Teter, Los Alamos National Laboratory

John Turner, Oak Ridge National Laboratory

Kilian Wasmer, Empa

Shoufeng Yang, KU Leuven

Page 59 Cite
Suggested Citation:"Appendix A: Registered Workshop Participants." National Academies of Sciences, Engineering, and Medicine. 2019. Data-Driven Modeling for Additive Manufacturing of Metals: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25481.
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Page 60 Cite
Suggested Citation:"Appendix A: Registered Workshop Participants." National Academies of Sciences, Engineering, and Medicine. 2019. Data-Driven Modeling for Additive Manufacturing of Metals: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25481.
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Additive manufacturing (AM) is the process in which a three-dimensional object is built by adding subsequent layers of materials. AM enables novel material compositions and shapes, often without the need for specialized tooling. This technology has the potential to revolutionize how mechanical parts are created, tested, and certified. However, successful real-time AM design requires the integration of complex systems and often necessitates expertise across domains. Simulation-based design approaches, such as those applied in engineering product design and material design, have the potential to improve AM predictive modeling capabilities, particularly when combined with existing knowledge of the underlying mechanics. These predictive models have the potential to reduce the cost of and time for concept-to-final-product development and can be used to supplement experimental tests.

The National Academies convened a workshop on October 24-26, 2018 to discuss the frontiers of mechanistic data-driven modeling for AM of metals. Topics of discussion included measuring and modeling process monitoring and control, developing models to represent microstructure evolution, alloy design, and part suitability, modeling phases of process and machine design, and accelerating product and process qualification and certification. These topics then led to the assessment of short-, immediate-, and long-term challenges in AM. This publication summarizes the presentations and discussions from the workshop.

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