Understanding and visualizing microstructure and microstructure variance as a stochastic process

By S. Niezgoda, Y. Yabansu, Surya R. Kalidindi. 2011

145 citations recorded by the source corpus.

Subfields: Computational Mechanics, Materials Chemistry, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/314400aaf2ebc426ba44104b48fcbc29fe6641a9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. David L. McDowell
2. Surya R. Kalidindi
3. Rampi Ramprasad
4. Rohit Batra
5. Le Song
6. Jitesh H. Panchal
7. A. Kanjarla
8. S. Niezgoda

In Materials Chemistry

1. Surya R. Kalidindi
2. David L. McDowell
3. Rohit Batra
4. Jitesh H. Panchal
5. Rampi Ramprasad
6. A. Kanjarla
7. Le Song
8. M. Priddy

In Mechanical Engineering

1. Surya R. Kalidindi
2. David L. McDowell
3. Jitesh H. Panchal
4. M. Priddy
5. Noah H. Paulson
6. A. Kanjarla
7. Rohit Batra
8. Rampi Ramprasad

Citations in the corpus

• Novel microstructure quantification framework for databasing, visualization, and analysis of microstructure data (2013, methodology): “Niezgoda [11] and Niezgoda, Yabansu and Kalidindi [12] formalized the description of microstructure via statistical metrics into a stochastic process interpretation, and developed the basic theory for the microstructure visualization space presented in this work.”
• Reduced-order structure-property linkages for polycrystalline microstructures based on 2-point statistics (2017, methodology): “Much of the prior work has been limited to materials with multiple distinct phases [1,68,70,73,74].”
• Computational microstructure characterization and reconstruction: Review of the state-of-the-art techniques (2018, background): “In continuum mechanics literature, a microstructure is called, depending on its size, either an RVE or an SVE (see below) [23,67,68].”
• Emerging materials intelligence ecosystems propelled by machine learning (2020, methodology): “…serving as descriptors for micro-structural images (obtained from either experiments or phase-field models) have been combined with dimensionality-reduction techniques such as principal component analysis to establish processing–structure– property–performance (PSPP) relations 168,169 .”
• Key computational modeling issues in Integrated Computational Materials Engineering (2013, methodology): “representations of the 2-point statistics [21,22], as shown in Fig.”
• Structure–property linkages using a data science approach: Application to a non-metallic inclusion/steel composite system (2015, methodology): “The values of bi provide important measures of the inherent variance among the members of the ensemble of microstructures [30].”