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Suggested Citation:"1 Introduction." 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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1

Introduction

Additive manufacturing (AM), the process in which a three-dimensional object is built by adding subsequent layers of materials, has the potential to revolutionize how mechanical parts are created, tested, and certified. AM enables novel material compositions and shapes, often without the need for specialized tooling. However, successful real-time AM design requires the integration of complex systems and often necessitates expertise across domains.

The complex design and processing systems that enable AM start with computer models. Since AM processes can be difficult to measure experimentally and empirical models for AM can be expensive to create, advanced fundamental models (including mechanistic data-driven reduced-order models and other validated theoretical and computational models) can be used to better understand underlying physical mechanisms. 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.

On October 24–26, 2018, the National Academies of Sciences, Engineering, and Medicine organized a workshop of experts from various communities within the United States and the European Union to discuss the frontiers of mechanistic data-driven modeling for AM of metals. The

Suggested Citation:"1 Introduction." 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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planning committee (shown on page v) helped to identify the workshop topics, invite speakers, and plan the agenda. The workshop was held at the Neue Materialien Fürth GmbH building of the Friedrich-Alexander-Universität Erlangen-Nürnberg in Fürth, Germany. This workshop was sponsored by the U.S. Department of Energy, the U.S. National Institute of Standards and Technology, Sandia National Laboratories, and Los Alamos National Laboratory.

Wing Kam Liu (Northwestern University), the chair of the planning committee, opened the workshop by discussing its four main topics:

  • 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.

The first 2 days of the workshop focused on presentations and panel discussions relating to the workshop themes. The third day centered on breakout groups that discussed some of the short-, intermediate-, and long-term challenges in AM.

This proceedings summarizes the presentations and discussions that took place during the workshop. The viewpoints expressed in this proceedings are those of individual workshop participants and do not necessarily represent the views of all workshop participants, the planning committee, or the National Academies of Sciences, Engineering, and Medicine.

ORGANIZATION OF THIS PROCEEDINGS

The following chapters in this proceedings summarize the workshop’s presentations and discussions. Chapter 2 describes the measurements and modeling for process monitoring control in AM. Chapter 3 provides an overview of developing models to represent microstructure evolution, alloy design, and part suitability. Chapter 4 focuses on modeling aspects of process and machine design. Chapter 5 discusses opportunities to accelerate product and process qualification and certification. Chapter 6 summarizes challenges raised during subgroup discussions and by individual participants. A list of registered workshop participants appears in Appendix A, and Appendix B includes the 3-day workshop agenda.

Suggested Citation:"1 Introduction." 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 1
Suggested Citation:"1 Introduction." 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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