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Extraction of mechanical properties of materials through deep learning from instrumented indentation
Instrumented indentation has been developed and widely utilized as one of the most versatile and practical means of extracting mechanical properties of materials. This method is particularly desirable for those applications where it is difficult to experimentally determine the mechanical properties...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7132259/ https://www.ncbi.nlm.nih.gov/pubmed/32179694 http://dx.doi.org/10.1073/pnas.1922210117 |
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author | Lu, Lu Dao, Ming Kumar, Punit Ramamurty, Upadrasta Karniadakis, George Em Suresh, Subra |
author_facet | Lu, Lu Dao, Ming Kumar, Punit Ramamurty, Upadrasta Karniadakis, George Em Suresh, Subra |
author_sort | Lu, Lu |
collection | PubMed |
description | Instrumented indentation has been developed and widely utilized as one of the most versatile and practical means of extracting mechanical properties of materials. This method is particularly desirable for those applications where it is difficult to experimentally determine the mechanical properties using stress–strain data obtained from coupon specimens. Such applications include material processing and manufacturing of small and large engineering components and structures involving the following: three-dimensional (3D) printing, thin-film and multilayered structures, and integrated manufacturing of materials for coupled mechanical and functional properties. Here, we utilize the latest developments in neural networks, including a multifidelity approach whereby deep-learning algorithms are trained to extract elastoplastic properties of metals and alloys from instrumented indentation results using multiple datasets for desired levels of improved accuracy. We have established algorithms for solving inverse problems by recourse to single, dual, and multiple indentation and demonstrate that these algorithms significantly outperform traditional brute force computations and function-fitting methods. Moreover, we present several multifidelity approaches specifically for solving the inverse indentation problem which 1) significantly reduce the number of high-fidelity datasets required to achieve a given level of accuracy, 2) utilize known physical and scaling laws to improve training efficiency and accuracy, and 3) integrate simulation and experimental data for training disparate datasets to learn and minimize systematic errors. The predictive capabilities and advantages of these multifidelity methods have been assessed by direct comparisons with experimental results for indentation for different commercial alloys, including two wrought aluminum alloys and several 3D printed titanium alloys. |
format | Online Article Text |
id | pubmed-7132259 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-71322592020-04-09 Extraction of mechanical properties of materials through deep learning from instrumented indentation Lu, Lu Dao, Ming Kumar, Punit Ramamurty, Upadrasta Karniadakis, George Em Suresh, Subra Proc Natl Acad Sci U S A Physical Sciences Instrumented indentation has been developed and widely utilized as one of the most versatile and practical means of extracting mechanical properties of materials. This method is particularly desirable for those applications where it is difficult to experimentally determine the mechanical properties using stress–strain data obtained from coupon specimens. Such applications include material processing and manufacturing of small and large engineering components and structures involving the following: three-dimensional (3D) printing, thin-film and multilayered structures, and integrated manufacturing of materials for coupled mechanical and functional properties. Here, we utilize the latest developments in neural networks, including a multifidelity approach whereby deep-learning algorithms are trained to extract elastoplastic properties of metals and alloys from instrumented indentation results using multiple datasets for desired levels of improved accuracy. We have established algorithms for solving inverse problems by recourse to single, dual, and multiple indentation and demonstrate that these algorithms significantly outperform traditional brute force computations and function-fitting methods. Moreover, we present several multifidelity approaches specifically for solving the inverse indentation problem which 1) significantly reduce the number of high-fidelity datasets required to achieve a given level of accuracy, 2) utilize known physical and scaling laws to improve training efficiency and accuracy, and 3) integrate simulation and experimental data for training disparate datasets to learn and minimize systematic errors. The predictive capabilities and advantages of these multifidelity methods have been assessed by direct comparisons with experimental results for indentation for different commercial alloys, including two wrought aluminum alloys and several 3D printed titanium alloys. National Academy of Sciences 2020-03-31 2020-03-16 /pmc/articles/PMC7132259/ /pubmed/32179694 http://dx.doi.org/10.1073/pnas.1922210117 Text en Copyright © 2020 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/ https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Physical Sciences Lu, Lu Dao, Ming Kumar, Punit Ramamurty, Upadrasta Karniadakis, George Em Suresh, Subra Extraction of mechanical properties of materials through deep learning from instrumented indentation |
title | Extraction of mechanical properties of materials through deep learning from instrumented indentation |
title_full | Extraction of mechanical properties of materials through deep learning from instrumented indentation |
title_fullStr | Extraction of mechanical properties of materials through deep learning from instrumented indentation |
title_full_unstemmed | Extraction of mechanical properties of materials through deep learning from instrumented indentation |
title_short | Extraction of mechanical properties of materials through deep learning from instrumented indentation |
title_sort | extraction of mechanical properties of materials through deep learning from instrumented indentation |
topic | Physical Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7132259/ https://www.ncbi.nlm.nih.gov/pubmed/32179694 http://dx.doi.org/10.1073/pnas.1922210117 |
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