Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches

By Zijiang Yang, Y. Yabansu, Dipendra Jha, W. Liao, Alok N. Choudhary, Surya R. Kalidindi, Ankit Agrawal. Acta Materialia, 2019

170 citations recorded by the source corpus.

Abstract Data-driven methods are attracting growing attention in the field of materials science. In particular, it is now becoming clear that machine learning approaches offer a unique avenue for successfully mining practically useful process-structure-property (PSP) linkages from a variety of materials data. Most previous efforts in this direction have relied on feature design (i.e., the identification of the salient features of the material microstructure to be included in the PSP linkages). However due to the rich complexity of features in most heterogeneous materials systems, it has been difficult to identify a set of consistent features that are transferable from one material system to another. With flexible architecture and remarkable learning capability, the emergent deep learning approaches offer a new path forward that circumvents the feature design step. In this work, we demonstrate the implementation of a deep learning feature-engineering-free approach to the prediction of the microscale elastic strain field in a given three-dimensional voxel-based microstructure of a high-contrast two-phase composite. The results show that deep learning approaches can implicitly learn salient information about local neighborhood details, and significantly outperform state-of-the-art methods.

Subfields: Civil and Structural Engineering, Materials Chemistry, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/0c9c729c374a82c0cfdcec630078be705d2a8ede

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

In Materials Chemistry

In Mechanics of Materials

Citations in the corpus

Recent advances and applications of deep learning methods in materials science (2021, background): “com/NU-CUCIS/LocalizationDL [262] MDGAN https://github.”
Deep learning model to predict complex stress and strain fields in hierarchical composites (2021, methodology): “While some earlier work has used convolutional neural networks to predict localization linkages for elastic deformation in composites ( 42 ), the results reported in this paper offers a method that is capable to deal with more generalized mechanical problems and can be more easily used to…”