Abstract
To analyze complex, heterogeneous materials, a fast and accurate method is needed. This means going beyond the classical finite element method, in a search for the ability to compute, with modest computational resources, solutions previously infeasible even with large cluster computers. In particular, this advance is motivated by composites design. Here, we apply similar principle to another complex, heterogeneous system: additively manufactured metals.
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Acknowledgements
Z.L., O.L.K., C.Y. and W.K.L. warmly thank the support from AFOSR grant No. FA9550-14-1-0032, National Institute of Standards and Technology and Center for Hierarchical Materials Design (CHiMaD) under grant No. 70NANB13Hl94 and 70NANB14H012, and DOE CF-ICME project under grant No. DE-EE0006867. O.L.K. thanks United States National Science Foundation (NSF) for their support through the NSF Graduate Research Fellowship Program (GRFP) under financial award number DGE-1324585.
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Liu, Z., Kafka, O.L., Yu, C., Liu, W.K. (2018). Data-Driven Self-consistent Clustering Analysis of Heterogeneous Materials with Crystal Plasticity. In: Oñate, E., Peric, D., de Souza Neto, E., Chiumenti, M. (eds) Advances in Computational Plasticity. Computational Methods in Applied Sciences, vol 46. Springer, Cham. https://doi.org/10.1007/978-3-319-60885-3_11
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