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• 100

  ▼a  Patel, Janki,
  ▼e  rapporteur.

• 245

  ▼a  Data-driven modeling for additive manufacturing of metals:
  ▼b  proceedings of a workshop /
  ▼c  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.

• 260

  ▼a  Washington, DC:
  ▼b  The National Academies Press,
  ▼c  [2019].

• 300

  ▼a  1 online resource (xii, 66 pages):
  ▼b  color illustrations.

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  ▼a  text
  ▼b  txt
  ▼2  rdacontent

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  ▼a  computer
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  ▼a  online resource
  ▼b  cr
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  ▼a  "A Workshop on the Frontiers of Mechanistic Data-Driven Modeling for Additive Manufacturing."

• 504

  ▼a  Includes bibliographical references.

• 505

  ▼a  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

• 520

  ▼a  "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

• 588

  ▼a  Print version record.

• 588

  ▼a  Online resource; title from PDF title page (National Academies Press, viewed October 24, 2019).

• 590

  ▼a  Master record variable field(s) change: 650

• 650

  ▼a  Additive manufacturing
  ▼v  Congresses.

• 650

  ▼a  Manufacturing processes
  ▼v  Congresses.

• 650

  ▼a  Materials
  ▼x  Additives
  ▼v  Congresses.

• 650

  ▼a  Materials
  ▼x  Technological innovations
  ▼v  Congresses.

• 650

  ▼a  Production management
  ▼x  Data processing
  ▼v  Congresses.

• 650

  ▼a  Manufacturing processes.

• 650

  ▼a  Manufacturing processes.
  ▼2  fast
  ▼0  (OCoLC)fst01008139

• 650

  ▼a  Materials
  ▼x  Technological innovations.
  ▼2  fast
  ▼0  (OCoLC)fst01011881

• 650

  ▼a  Production management
  ▼x  Data processing.
  ▼2  fast
  ▼0  (OCoLC)fst01078313

• 655

  ▼a  Electronic books.

• 655

  ▼a  Conference papers and proceedings.
  ▼2  fast
  ▼0  (OCoLC)fst01423772

• 710

  ▼a  National Academies of Sciences, Engineering, and Medicine (U.S.).
  ▼b  National Materials and Manufacturing Board,
  ▼e  issuing body.

• 710

  ▼a  National Academies of Sciences, Engineering, and Medicine (U.S.).
  ▼b  Board on Mathematical Sciences and Analytics,
  ▼e  issuing body.

• 711

  ▼a  Frontiers of Mechanistic Data-Driven Modeling for Additive Manufacturing (Workshop)
  ▼d  (2018 :
  ▼c  Fü̈rth, Germany)
  ▼j  issuing body.

• 776

  ▼i  Print version:
  ▼a  National Academies of Sciences, Engineering, and Medicine.
  ▼t  Data-Driven Modeling for Additive Manufacturing of Metals : Proceedings of a Workshop.
  ▼d  Washington, D.C. : National Academies Press, ©2019,
  ▼z  9780309494205

• 856

  ▼3  EBSCOhost
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  ▼a  Askews and Holts Library Services
  ▼b  ASKH
  ▼n  AH36833482

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  ▼a  ProQuest Ebook Central
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  ▼a  EBSCOhost
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  ▼a  YBP Library Services
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  ▼a  YBP Library Services
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