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Journal ArticleDOI

Calibrated localization relationships for elastic response of polycrystalline aggregates

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TLDR
In this article, a major extension to the Materials Knowledge System (MKS) framework is presented, which allows a computationally efficient treatment of a significantly more complex local state of the material, i.e. crystal lattice orientation.
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This article is published in Acta Materialia.The article was published on 2014-12-01. It has received 73 citations till now. The article focuses on the topics: Representative elementary volume.

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Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets

TL;DR: This work designs and implements a deep learning approach to model an elastic homogenization structure-property linkage in a high contrast composite material system and demonstrates that this end-to-end framework can predict the effective stiffness of high contrast elastic composites with a wide of range of microstructures, while exhibiting high accuracy and low computational cost for new evaluations.
Journal ArticleDOI

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics

TL;DR: It is shown that experiment- and simulation-based data mining in combination with machine leaning tools provide exceptional opportunities to enable highly reliant identification of fundamental interrelations within materials for characterization and optimization in a scale-bridging manner.
Journal ArticleDOI

Materials Data Science: Current Status and Future Outlook

TL;DR: The concept of process-structure-property (PSP) linkages is introduced and illustrated how the determination of PSPs is one of the main objectives of materials data science.
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Structure–property linkages using a data science approach: Application to a non-metallic inclusion/steel composite system

TL;DR: In this article, the feasibility of data-driven surrogate models for inelastic effective properties of composite materials is demonstrated for the first time, based on a data science approach, using an ensemble of representative microstructures and using established physics-based simulation tools.
Journal ArticleDOI

Reduced-order structure-property linkages for polycrystalline microstructures based on 2-point statistics

TL;DR: In this article, a novel framework is described for extracting S-P linkages in polycrystalline microstructures that are obtained using 2-point spatial correlations to quantify the material's microstructure, and principal component analysis (PCA) to represent this information in a reduced dimensional space.
References
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Book

Discrete-Time Signal Processing

TL;DR: In this paper, the authors provide a thorough treatment of the fundamental theorems and properties of discrete-time linear systems, filtering, sampling, and discrete time Fourier analysis.
MonographDOI

Mechanical Behavior of Materials

TL;DR: A balanced mechanics-materials approach and coverage of the latest developments in biomaterials and electronic materials, the new edition of this popular text is the most thorough and modern book available for upper-level undergraduate courses on the mechanical behavior of materials as discussed by the authors.
Book

Materials Properties Handbook: Titanium Alloys

TL;DR: The Materials Properties Handbook: Titanium Alloys as discussed by the authors provides a data base for information on titanium and its alloys, and the selection of specific alloys for specific applications, including applications, physical properties, corrosion, mechanical properties (including design allowances where available), fatigue, fracture properties, and elevated temperature properties.
Journal ArticleDOI

A numerical method for computing the overall response of nonlinear composites with complex microstructure

TL;DR: An alternate method based on Fourier series which avoids meshing and which makes direct use of microstructure images is proposed, based on the exact expression of the Green function of a linear elastic and homogeneous comparison material.
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