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Modeling and Simulation for Additive Manufacturing

Albert To, University of Pittsburgh

Adrian Lew, Stanford University

Kyle Johnson, Sandia National Laboratories

Andreas Lundback, Lulea Institute of Technology

Wing Kam Liu, Northwestern University

Stefan Kollmannsberger, Technical University of Munich

Gregory Wagner, Northwestern University

Wei Cai, Stanford University

Robert Ferencz, Lawrence Livermore National Laboratory

John Michopoulos, Naval Research Laboratory

John Emery, Sandia National Laboratory

Theron Rodgers, Sandia National Laboratory

Dan Moser, Sandia National Laboratory

Michael Stender, Sandia National Laboratory

Various additive manufacturing (AM) techniques including 3D printing have been developed to manufacture complex-shaped components with well-controlled precision.  Sophisticated AM techniques often require systematic modeling and simulation efforts during the design stage and for the purpose of part qualification/certification.  The objective of this minisymposium is to provide a platform to discuss recently developed modeling and simulation techniques for AM, including experimental calibration and validation efforts for the process.  The topics include (but are not limited to): 
• Multiphysics simulation techniques for additive maufacturing
• Part-scale and multiscale simulation of the manufacturing process to predict residual stress/distortion, surface topology, and microstructure including defects
• Microstructure prediction and optimization
• Data-driven approach for simulation acceleration 
• Combined simulation and in-situ monitoring for rapid build qualification 
• Effects of microstructure and defects on mechanical properties 
• Feedback control for minimizing defects and residual stress in as-built structures 
• AM-oriented topology optimization 
• Modeling and simulation of functionally graded materials, tissue engineering scaffolds, bioinspired composites, bi-material joints, etc 
Typically, computational modeling and simulation for any AM processes (e.g. laser sintering/melting, electron beam melting, form deposition modeling, stereolithography, binder jetting) and materials (e.g. metals, plastics, ceramics and their composites as well as biological materials) are welcome.
• Integration of feedback and/or feedforward control methods and process maps for minimizing the presence of undesirable features such as defects and residual stresses in as-built parts.
• Simulation of the manufacturing process for smart materials, sensors, and nano-devices.
• Modeling and prototyping of non-traditional AM processes enabling 2D and 3D material activation by breaking the point-by-point, line-by-line, layer-by-layer paradigm.