Microstructure Sensitive Design for Performance Optimization

By David T. Fullwood, S. Niezgoda, B. Adams, Surya R. Kalidindi. 2012

458 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Civil and Structural Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/fe1c61b73eee5a17eb180c0ac09c5edddd6e0e1a

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In Aerospace Engineering

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Citations in the corpus

Novel microstructure quantification framework for databasing, visualization, and analysis of microstructure data (2013, background): “Equation (4) is readily identified as a convolution integral which is readily computed via Fourier transform techniques [22, 25].”
Multiscale structural topology optimization with an approximate constitutive model for local material microstructure (2015, unknown): “1 2 3 4 5 6 7 Substeps of the iterative resolution Unreduced strategy [1] 10 mins/substep”
Bi-directional Evolutionary Structural Optimization on Advanced Structures and Materials: A Comprehensive Review (2016, background): “Meanwhile, the fast progress made in the field of material science allows us to control the material microstructure composition to an unprecedented extent [35, 152].”
Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets (2018, unknown): “of desired property combinations [4,26,27]).”
Reduced-order structure-property linkages for polycrystalline microstructures based on 2-point statistics (2017, methodology): “[5,16,57] have proposed a discretized description of the microstructure function Mn s based on primitive binning of both the spatial domain and the local state space, i.”
Key computational modeling issues in Integrated Computational Materials Engineering (2013, background): “A number of different measures of the spatial correlations in the microstructure have been proposed and utilized in literature [19,20].”
Materials informatics (2018, methodology): “This was accomplished mainly through the use of the formalism of the n-point spatial correlations (Adams et al. 2012) for quantifying the material internal structure, followed by the use of dimensionality reduction approaches (e.g., principal component analyses).”
Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches (2019, background): “This neighborhood information is usually referred as the higher-order microstructure information [51,64].”
Computational microstructure characterization and reconstruction: Review of the state-of-the-art techniques (2018, background): “Ergodic hypothesis : A hypothesis implying that monitoring a stochastic system (e.g., an evolving microstructure) over a long period of time would provide the same statistical measures as taking many independent realizations of that system over time [7].”
Structure–property linkages using a data science approach: Application to a non-metallic inclusion/steel composite system (2015, background): “Although we might expect a number of redundancies in the expression of the n-point spatial correlations, these have not yet been precisely identified [20,59].”