Structure–property linkages using a data science approach: Application to a non-metallic inclusion/steel composite system

By Akash Gupta, Ahmet Cecen, S. Goyal, Amarendra K. Singh, Surya R. Kalidindi. 2015

186 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Mechanics of Materials, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c8ba9d7cd2707946a1803353e1b36648c10b6577

How researchers see this paper

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

In Civil and Structural Engineering

1. David L. McDowell
2. Chengping Rao
3. M. Priddy
4. Noah H. Paulson
5. Yang Liu
6. Magdalena Ortiz
7. Surya R. Kalidindi
8. Seeram Ramakrishna

In Mechanics of Materials

1. David L. McDowell
2. Surya R. Kalidindi
3. M. Priddy
4. Noah H. Paulson
5. Y. Yabansu
6. Chengping Rao
7. Yang Liu
8. Magdalena Ortiz

In Statistics and Probability

1. M. Priddy
2. Noah H. Paulson
3. David L. McDowell
4. Surya R. Kalidindi
5. Magdalena Ortiz
6. Chengping Rao
7. Yang Liu
8. Trenton Kirchdoerfer

Citations in the corpus

• Materials informatics (2018, background): “…effective diffusivity (Çeçen et al. 2014), the two-phase microstructures in a steel-inclusion material system to their effective plastic properties (Gupta et al. 2015), the development and demonstration of novel high throughput experimental assays for recovering PSP linkages in dual-phase steels…”
• Data-driven computational mechanics (2015, background): “These approaches have been extended to the generation of multi-scale modeling correlations between macroscopic and microscopic constitutive properties [4–8].”
• 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].”
• The effects of non-metallic inclusions on properties relevant to the performance of steel in structural and mechanical applications (2019, background): “[23] Gupta A, Cecen A, Goyal S, Singh AK, Kalidindi SR.”
• Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets (2018, methodology): “In the sophisticated physics-inspired approaches, the reduced-order representation of spatial correlations in the microstructure are fit to the property values through standard regression methods [7,9,60].”
• Three-dimensional convolutional neural network (3D-CNN) for heterogeneous material homogenization (2020, methodology): “This framework is characterized with computational efficiency, data-driven property and remarkable accuracy in a variety of works [28–30, 32, 33].”