Data-driven self-consistent clustering analysis of heterogeneous materials with crystal plasticity

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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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Liu, Z., Kafka, O. L., Yu, C., & Liu, W. K. (2018). Data-driven self-consistent clustering analysis of heterogeneous materials with crystal plasticity. In Computational Methods in Applied Sciences (Vol. 46, pp. 221–242). Springer Netherland. https://doi.org/10.1007/978-3-319-60885-3_11

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