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A strategy for dimensionality reduction and data analysis applied to microstructure–property relationships of nanoporous metals
Citation Link: https://doi.org/10.15480/882.3617
Publikationstyp
Journal Article
Date Issued
2021-04-07
Sprache
English
Author(s)
TORE-DOI
TORE-URI
Journal
Volume
14
Issue
8
Article Number
1822
Citation
Materials 14 (8): 1822 (2021-04-07)
Publisher DOI
Scopus ID
Publisher
MDPI
Nanoporous metals, with their complex microstructure, represent an ideal candidate for the development of methods that combine physics, data, and machine learning. The preparation of nanporous metals via dealloying allows for tuning of the microstructure and macroscopic mechanical properties within a large design space, dependent on the chosen dealloying conditions. Specifically, it is possible to define the solid fraction, ligament size, and connectivity density within a large range. These microstructural parameters have a large impact on the macroscopic mechanical behavior. This makes this class of materials an ideal science case for the development of strategies for dimensionality reduction, supporting the analysis and visualization of the underlying structure–property relationships. Efficient finite element beam modeling techniques were used to generate ~200 data sets for macroscopic compression and nanoindentation of open pore nanofoams. A strategy consisting of dimensional analysis, principal component analysis, and machine learning allowed for data mining of the microstructure–property relationships. It turned out that the scaling law of the work hardening rate has the same exponent as the Young’s modulus. Simple linear relationships are derived for the normalized work hardening rate and hardness. The hardness to yield stress ratio is not limited to 1, as commonly assumed for foams, but spreads over a large range of values from 0.5 to 3.
Subjects
Data mining
FE-beam model
Hardness
Machine learning
Mechanical properties
Microcompression
Nanoindentation
Nanoporous metals
Open-pore foams
Principal component analysis
Structure–property relationship
DDC Class
600: Technik
620: Ingenieurwissenschaften
Funding Organisations
Publication version
publishedVersion
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