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

Yang, Zijiang; Yabansu, Yuksel C.; Jha, Dipendra; Liao, Wei; Choudhary, Alok; Kalidindi, Surya R.; Agrawal, Ankit

doi:10.1016/j.actamat.2018.12.045

Cited by 146 publications

(69 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Yang et al [30] present a novel feature-engineering-free approach for localization using deep learning. They used two datasets of contrast-10 (2500 MVEs) and contrast-50 (3000 MVEs) of size 21 × 21 × 21 with varying volume fraction and periodic boundary conditions, and were split into training, validation, and test sets.…”

Section: Multiscale Homogenization and Localization Linkages In High-mentioning

confidence: 99%

“…Although 3-D CNNs could be used for this problem as well, the dataset here is almost four orders of magnitude larger, and since 3-D CNNs are much more computationally expensive, the authors in Ref. 30 designed a neat workaround to be able to use 2-D CNNs for localization. They accomplished this by treating the 3-D image of size 11 × 11 × 11 as 11 channels of a 2-D images of size 11 × 11, perpendicular to the maximum principal strain direction.…”

Section: Multiscale Homogenization and Localization Linkages In High-mentioning

confidence: 99%

“…In this paper, we discuss some of the recent advances in deep materials informatics for exploring PSPP linkages in materials, after a brief introduction to the basics of deep learning, and its challenges and opportunities. Illustrative examples of deep materials informatics that we review in this paper include learning the chemistry of materials using only elemental composition, [24] structure-aware property prediction, [25,26] crystal structure prediction, [27] learning multiscale homogenization [28,29] and localization [30] linkages in high-contrast composites, structure characterization [31,32] and quantification, [33,34] and microstructure reconstruction [35] and design. [36] We also discuss the future outlook and envisioned impact of deep learning in materials science before summarizing and concluding the paper.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep materials informatics: Applications of deep learning in materials science

2019

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Multiscale Homogenization and Localization Linkages In High-mentioning

confidence: 99%

Section: Multiscale Homogenization and Localization Linkages In High-mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep materials informatics: Applications of deep learning in materials science

2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…D ata-driven methods such as machine learning (ML) and statistical analysis (SA) are efficient toolsets for extracting process-structure-property relation or for designsynthesis-characterization of materials [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] . ML and SA are able to address large and complex tasks by focusing on the most relevant information in an overwhelming quantity of data while providing similar or better accuracy to the finite element analysis (FEA) and experiment [19][20][21][22] .…”

mentioning

confidence: 99%

“…Tiryaki and Aydin employed ML models to predict the compression strength of heat-treated woods 27 . Deep learning approaches were used for mining structure-property linkages in high contrast composites from simulation datasets, and to establish structure-property localization linkages for elastic deformation of three-dimensional high contrast composites 16,18 .…”

mentioning

confidence: 99%

Designing bioinspired brick-and-mortar composites using machine learning and statistical learning

2020

View full text Add to dashboard Cite

The brick-and-mortar structure inspired by nature, such as in nacre, is considered one of the most optimal designs for structural composites. Given the large number of design possibilities, extensive computational work is required to guide their manufacturing. Here, we propose a computational framework that combines statistical analysis and machine learning with finite element analysis to establish structure-property design strategies for brick-andmortar composites. Approximately 20,000 models with different geometrical designs were categorized into good and bad based on their failure modes, with statistical analysis of the results used to find the importance of each feature. Aspect ratio of the bricks and horizontal mortar thickness were identified as the main influencing features. A decision tree machine learning model was then established to draw the boundaries of good design space. This approach might be used for the design of brick-and-mortar composites with improved mechanical properties.

show abstract

Deep Learning in Mechanical Metamaterials: From Prediction and Generation to Inverse Design

et al. 2023

View full text Add to dashboard Cite

Mechanical metamaterials are meticulously designed structures with exceptional mechanical properties determined by their microstructures and constituent materials. Tailoring their material and geometric distribution unlocks the potential to achieve unprecedented bulk properties and functions. However, current mechanical metamaterial design considerably relies on experienced designers' inspiration through trial and error, while investigating their mechanical properties and responses entails time‐consuming mechanical testing or computationally expensive simulations. Nevertheless, recent advancements in deep learning have revolutionized the design process of mechanical metamaterials, enabling property prediction and geometry generation without prior knowledge. Furthermore, deep generative models can transform conventional forward design into inverse design. Many recent studies on the implementation of deep learning in mechanical metamaterials are highly specialized, and their pros and cons may not be immediately evident. This critical review provides a comprehensive overview of the capabilities of deep learning in property prediction, geometry generation, and inverse design of mechanical metamaterials. Additionally, this review highlights the potential of leveraging deep learning to create universally applicable datasets, intelligently designed metamaterials, and material intelligence. This article is expected to be valuable not only to researchers working on mechanical metamaterials but also those in the field of materials informatics.This article is protected by copyright. All rights reserved

show abstract

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

Cited by 146 publications

References 47 publications

Deep materials informatics: Applications of deep learning in materials science

Deep materials informatics: Applications of deep learning in materials science

Designing bioinspired brick-and-mortar composites using machine learning and statistical learning

Deep Learning in Mechanical Metamaterials: From Prediction and Generation to Inverse Design

Contact Info

Product

Resources

About