This dataset contains 8900 microscale volume elements (MVEs) and their associated effective stiffness parameter used for establishing structure-property linkages of high contrast elastic composites. The MVEs in this dataset were used in three publications listed below.
1. P. Fernandez-Zelaia, Y.C. Yabansu, S.R. Kalidindi, A comparative study of the efficacy of local/global and parametric/nonparametric machine learning methods for establishing structure-property linkages in high contrast 3-D elastic composites. Integrating Materials and Manufacturing Innovation, 2019. 8: p. 67-81
2. Z. Yang, Y.C. Yabansu, R. Al-Bahrani, W. Liao, A.N. Choudhary, S.R. Kalidindi, A. Agrawal, Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets. Computational Materials Science, 2018. 151: p. 278-287
3. A. Cecen, H. Dai, Y.C. Yabansu, S.R. Kalidindi, L. Song, Material structure-property linkages using three-dimensional convolutional neural network. Acta Materialia, 2018. 146: p. 76-84
The MVEs have a uniformly discretized grid of 51x51x51 voxels. The material is considered a two-phase composite material. Both phases are isotropically elastic and the contrast between the Young's modulus of phases was set to 50. The homogenized elastic stiffness parameter is for C11 component of elastic stiffness tensor. FE simulations were executed by assuming periodic boundary conditions in such a way that only the normal strain component in x direction at macroscale level was nonzero. Further details about the generation of microstructures and FE simulations can be found in the above references.
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