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Suggested Citation:"Front Matter." 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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Suggested Citation:"Front Matter." 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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Suggested Citation:"Front Matter." 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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Suggested Citation:"Front Matter." 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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Suggested Citation:"Front Matter." 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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Suggested Citation:"Front Matter." 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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Suggested Citation:"Front Matter." 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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Suggested Citation:"Front Matter." 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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Suggested Citation:"Front Matter." 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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Suggested Citation:"Front Matter." 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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Suggested Citation:"Front Matter." 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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Suggested Citation:"Front Matter." 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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Data-Driven Modeling for Additive Manufacturing of Metals PROCEEDINGS OF A WORKSHOP Janki Patel, Rapporteur Board on Mathematical Sciences and Analytics National Materials and Manufacturing Board Division on Engineering and Physical Sciences

THE NATIONAL ACADEMIES PRESS 500 Fifth Street, NW Washington, DC 20001 This activity was supported by the U.S. National Institute of Standards and Tech- nology (Contract No. SB134117CQ0017), the Department of Energy, Los Alamos National Laboratory, and Sandia National Laboratories. Any opinions, findings, conclusions, or recommendations expressed in this publication do not necessar- ily reflect the views of any organization or agency that provided support for the project. International Standard Book Number-13: 978-0-309-49420-5 International Standard Book Number-10: 0-309-49420-6 Digital Object Identifier: https://doi.org/10.17226/25481 Additional copies of this publication are available for sale from the National Acad- emies Press, 500 Fifth Street, NW, Keck 360, Washington, DC 20001; (800) 624-6242 or (202) 334-3313; http://www.nap.edu. Copyright 2019 by the National Academy of Sciences. All rights reserved. Printed in the United States of America Suggested citation: National Academies of Sciences, Engineering, and ­ edicine. M 2019. Data-Driven Modeling for Additive Manufacturing of Metals: Proceedings of a Workshop. Washington, DC: The National Academies Press. https://doi. org/10.17226/25481.

The National Academy of Sciences was established in 1863 by an Act of Congress, signed by President Lincoln, as a private, nongovernmental institution to advise the nation on issues related to science and technology. Members are elected by their peers for outstanding contributions to research. Dr. Marcia McNutt is president. The National Academy of Engineering was established in 1964 under the charter of the National Academy of Sciences to bring the practices of engineering to advising the nation. Members are elected by their peers for extraordinary contributions to engineering. Dr. John L. Anderson is president. The National Academy of Medicine (formerly the Institute of Medicine) was established in 1970 under the charter of the National Academy of Sciences to advise the nation on medical and health issues. Members are elected by their peers for distinguished contributions to medicine and health. Dr. Victor J. Dzau is president. The three Academies work together as the National Academies of Sciences, Engineering, and Medicine to provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions. The National Academies also encourage education and research, recognize outstanding contributions to knowledge, and increase public understanding in matters of science, engineering, and medicine. Learn more about the National Academies of Sciences, Engineering, and Medicine at www.nationalacademies.org.

Consensus Study Reports published by the National Academies of Sciences, Engineering, and Medicine document the evidence-based consensus on the study’s statement of task by an authoring committee of experts. Reports typically include findings, conclusions, and recommendations based on information gathered by the committee and the committee’s deliberations. Each report has been subjected to a rigorous and independent peer-review process and it represents the position of the National Academies on the statement of task. Proceedings published by the National Academies of Sciences, Engineering, and Medicine chronicle the presentations and discussions at a workshop, symposium, or other event convened by the National Academies. The statements and opinions contained in proceedings are those of the participants and are not endorsed by other participants, the planning committee, or the National Academies. For information about other products and activities of the National Academies, please visit www.nationalacademies.org/about/whatwedo.

PLANNING COMMITTEE ON THE WORKSHOP ON THE FRONTIERS OF MECHANISTIC DATA-DRIVEN MODELING FOR ADDITIVE MANUFACTURING CAROLIN KÖRNER, Friedrich-Alexander Universität Erlangen- Nürnberg, Co-Chair WING KAM LIU, Northwestern University, Co-Chair TAHANY EL-WARDANY, United Technologies Research Center ADE MAKINDE, General Electric Global Research Center MUSTAFA MEGAHED, ESI Group CELIA MERZBACHER, SRI International NANCY REID, NAS,1 University of Toronto JENS TELGKAMP, Airbus Operations GmbH KAREN E. WILLCOX, University of Texas, Austin Staff MICHELLE SCHWALBE, Director, Board on Mathematical Sciences and Analytics, Workshop Co-Director ERIK SVEDBERG, Senior Program Officer, National Materials and Manufacturing Board, Workshop Co-Director BETH DOLAN, Financial Manager JANKI PATEL, Research Associate 1 Member, National Academy of Sciences. v

BOARD ON MATHEMATICAL SCIENCES AND ANALYTICS MARK L. GREEN, University of California, Los Angeles, Chair HÉLÈNE BARCELO, Mathematical Sciences Research Institute JOHN R. BIRGE, NAE,1 University of Chicago W. PETER CHERRY, NAE, Independent Consultant DAVID S.C. CHU, Institute for Defense Analyses RONALD R. COIFMAN, NAS,2 Yale University JAMES (JIM) CURRY, University of Colorado, Boulder SHAWNDRA HILL, Microsoft Research LYDIA KAVRAKI, NAM,3 Rice University TAMARA KOLDA, Sandia National Laboratories JOSEPH A. LANGSAM, University of Maryland DAVID MAIER, Portland State University LOIS CURFMAN McINNES, Argonne National Laboratory JILL C. PIPHER, Brown University ELIZABETH A. THOMPSON, NAS, University of Washington CLAIRE J. TOMLIN, NAE, University of California, Berkeley LANCE A. WALLER, Emory University KAREN E. WILLCOX, University of Texas, Austin Staff MICHELLE SCHWALBE, Director TYLER KLOEFKORN, Program Officer LINDA CASOLA, Associate Program Officer ADRIANNA HARGROVE, Financial Manager SELAM ARAIA, Program Assistant 1 Member, National Academy of Engineering. 2 Member, National Academy of Sciences. 3 Member, National Academy of Medicine. vi

NATIONAL MATERIALS AND MANUFACTURING BOARD BEN WANG, Georgia Institute of Technology, Chair THERESA KOTANCHEK, Evolved Analytics, LLC, Vice Chair RODNEY C. ADKINS, NAE,1 IBM Corporate Strategy (Retired) CRAIG ARNOLD, Princeton University JIM C.I. CHANG, National Cheng Kung University, Tainan, Taiwan THOMAS M. DONNELLAN, The Pennsylvania State University STEPHEN R. FORREST, NAS2/NAE, University of Michigan ERICA R.H. FUCHS, Carnegie Mellon University DAVID C. LARBALESTIER, NAE, Florida State University MICHAEL MAHER, Maher and Associates, LLC ROBERT D. MILLER, NAE, IBM Almaden Research Center EDWARD MORRIS, Consequence Consulting, LLC NICHOLAS A. PEPPAS, NAE/NAM,3 University of Texas, Austin TRESA M. POLLOCK, NAE, University of California, Santa Barbara GREGORY TASSEY, University of Washington HAYDN WADLEY, University of Virginia STEVEN J. ZINKLE, NAE, University of Tennessee, Knoxville Staff JAMES LANCASTER, Director ERIK SVEDBERG, Senior Program Officer NEERAJ P. GORKHALY, Associate Program Officer HEATHER LOZOWSKI, Financial Associate BETH DOLAN, Financial Associate AMISHA JINANDRA, Research Associate JOSEPH PALMER, Senior Project Assistant 1 Member, National Academy of Engineering. 2 Member, National Academy of Sciences. 3 Member, National Academy of Medicine. vii

Acknowledgment of Reviewers This Proceedings of a Workshop was reviewed in draft form by indi- viduals chosen for their diverse perspectives and technical expertise. The purpose of this independent review is to provide candid and critical comments that will assist the National Academies of Sciences, Engineer- ing, and Medicine in making each published proceedings as sound as possible and to ensure that it meets the institutional standards for quality, objectivity, evidence, and responsiveness to the charge. The review com- ments and draft manuscript remain confidential to protect the integrity of the process. We thank the following individuals for their review of this proceedings: Bianca Maria Colosimo, Politecnico di Milano; Joseph M. DeSimone, NAS1/NAE2/NAM,3 University of North Carolina, Chapel Hill; and John Turner, Oak Ridge National Laboratory. Although the reviewers listed above provided many constructive comments and suggestions, they were not asked to endorse the content of the proceedings nor did they see the final draft before its release. The 1 Member, National Academy of Sciences. 2 Member, National Academy of Engineering. 3 Member, National Academy of Medicine. ix

x ACKNOWLEDGMENT OF REVIEWERS review of this proceedings was overseen by Mark C. Hersam, North- western University. He was responsible for making certain that an inde- pendent examination of this proceedings was carried out in accordance with the standards of the National Academies and that all review com- ments were carefully considered. Responsibility for the final content rests entirely with the rapporteur and the National Academies.

Contents 1 INTRODUCTION 1 Organization of This Proceedings, 2 2 PROCESS MONITORING AND CONTROL 3 Measurements and Modeling for Process Monitoring and Control, 4 Measurement Science for Process Monitoring and Control, 9 Simulations: A Chance for Knowledge-Based Improvement of Additive Manufacturing, 11 Discussion, 13 References, 15 3 MICROSTRUCTURE EVOLUTION, ALLOY DESIGN, AND PART SUITABILITY 19 Measurements for Additive Manufacturing of Metals, 19 Predicting Material State and Performance of Additively Manufactured Parts, 22 Discussion, 24 References, 27 xi

xii CONTENTS 4 PROCESS AND MACHINE DESIGN 28 Modeling Phases of Process and Machine Design, 29 Current State of Commercial Powder-Bed Additive Machines—AM Machine Design Issues Impacting Build-to-Build and Part-to-Part Variability, 33 Modeling Challenges and Opportunities at the Part Level, 35 Discussion, 39 References, 39 5 PRODUCT AND PROCESS QUALIFICATION AND CERTIFICATION 41 Process Qualification and Technological Validation, from Casting to Additive, 41 Modeling and Simulation, 43 Discussion, 44 Reference, 46 6 SUMMARY OF CHALLENGES FROM SUBGROUP DISCUSSIONS AND PARTICIPANT COMMENTS 47 Measurements and Modeling for Process Monitoring and Control, 48 Developing Models to Represent Microstructure Evolution, Alloy Design, and Part Suitability, 50 Modeling Aspects of Process and Machine Design, 52 Accelerating Product and Process Qualification and Certification, 54 Individual Response Results, 56 References, 56 APPENDIXES A Registered Workshop Participants 59 B Workshop Agenda 61 C Workshop Statement of Task 66

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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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