Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches. (March 2019)

• Record Type: Journal Article
• Authors: Yang, Zijiang; Yabansu, Yuksel C.; Jha, Dipendra; Liao, Wei-keng; Choudhary, Alok N.; Kalidindi, Surya R.; Agrawal, Ankit
• Is Part Of: Acta materialia. Volume 166(2019)
• Page Start: 335
• Page End: 345
• Publication Date: 2019-03
• Subjects: Materials informatics -- Convolutional neural networks -- Deep learning -- Localization -- Structure-property linkages; Materials -- Periodicals; Materials science -- Periodicals; Materials -- Mechanical properties -- Periodicals; Metallurgy -- Periodicals; Chemistry, Inorganic -- Periodicals; 620.112
• DOI: 10.1016/j.actamat.2018.12.045
• Languages: English
• ISSNs: 1359-6454

Abstract

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. Graphical abstract: Image 1