Journal
MECHANICS OF MATERIALS
Volume 165, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.mechmat.2021.104156
Keywords
Multiscale modeling; Materials by design; Machine learning; Uncertainty quantification
Categories
Funding
- Army Research Laboratory, United States [W911NF-12-2-0022]
- Air Force Office of Scientific Research, United States under the MURI Award [FA9550-16-1-0566]
- Air Force Office of Scientific Research, United States through the Center of Excellence on High-Rate Deformation Physics of Heterogeneous Materials [FA9550-12-10091]
- Deutsche Forschungsgemeinschaft, Germany through the Sonderforschungsbereich 1060 'The mathematics of emergent effects'
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In recent decades, various methods have been used to accelerate the development of materials targeted towards specific applications. The choice of a particular material system is based on the performance required for a specific application, which is optimized through processing. The structure is then designed by characterizing the material in detail and utilizing this information for system-level simulations and optimization.
The recent decades have seen various attempts at accelerating the process of developing materials targeted towards specific applications. The performance required for a particular application leads to the choice of a particular material system whose properties are optimized by manipulating its underlying microstructure through processing. The specific configuration of the structure is then designed by characterizing the material in detail, and using this characterization along with physical principles in system level simulations and optimization. These have been advanced by multiscale modeling of materials, high-throughput experimentations, materials data-bases, topology optimization and other ideas. Still, developing materials for extreme applications involving large deformation, high strain rates and high temperatures remains a challenge. This article reviews a number of recent methods that advance the goal of designing materials targeted by specific applications.
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