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Data-driven modeling for additive manufacturing of metals : proceedings of a workshop
Patel, Janki
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| 자료유형 | E-BOOK |
|---|---|
| 서명/저자사항 | 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 of Sciences, Engineering, Medicine. |
| 개인저자 | Patel, Janki,rapporteur. |
| 단체저자명 | National Academies of Sciences, Engineering, and Medicine (U.S.). National Materials and Manufacturing Board,issuing body. National Academies of Sciences, Engineering, and Medicine (U.S.). Board on Mathematical Sciences and Analytics,issuing body. |
| 발행사항 | Washington, DC: The National Academies Press, [2019]. |
| 형태사항 | 1 online resource (xii, 66 pages): color illustrations. |
| 기타형태 저록 | Print version: National Academies of Sciences, Engineering, and Medicine. Data-Driven Modeling for Additive Manufacturing of Metals : Proceedings of a Workshop. Washington, D.C. : National Academies Press, ©2019 9780309494205 |
| ISBN | 9780309494212 0309494214 9780309494236 0309494230 |
| 일반주기 |
"A Workshop on the Frontiers of Mechanistic Data-Driven Modeling for Additive Manufacturing."
|
| 서지주기 | Includes bibliographical references. |
| 내용주기 | Introduction -- Process monitoring and control -- Microstructure evolution, alloy design, and part suitability -- Process and machine design -- Product and process qualification and certification -- Summary of challenges from subgroup discussions and participant comments -- Appendixes |
| 요약 | "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"--Publisher's description |
| 회의명 | Frontiers of Mechanistic Data-Driven Modeling for Additive Manufacturing (Workshop) (2018 : Fü̈rth, Germany) |
| 일반주제명 | Additive manufacturing --Congresses. Manufacturing processes --Congresses. Materials --Additives --Congresses. Materials --Technological innovations --Congresses. Production management --Data processing --Congresses. Manufacturing processes. Manufacturing processes. Materials --Technological innovations. Production management --Data processing. |
| 언어 | 영어 |
| 바로가기 | URL |
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