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Bridging Fidelities to Predict Nanoindentation Tip Radii Using Interpretable Deep Learning Models
As the need for miniaturized structural and functional materials has increased, the need for precise materials characterizaton has also expanded. Nanoindentation is a popular method that can be used to measure material mechanical behavior which enables high-throughput experiments and, in some cases,...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9122886/ https://www.ncbi.nlm.nih.gov/pubmed/35611344 http://dx.doi.org/10.1007/s11837-022-05233-z |
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author | Trost, Claus O. W. Zak, Stanislav Schaffer, Sebastian Saringer, Christian Exl, Lukas Cordill, Megan J. |
author_facet | Trost, Claus O. W. Zak, Stanislav Schaffer, Sebastian Saringer, Christian Exl, Lukas Cordill, Megan J. |
author_sort | Trost, Claus O. W. |
collection | PubMed |
description | As the need for miniaturized structural and functional materials has increased, the need for precise materials characterizaton has also expanded. Nanoindentation is a popular method that can be used to measure material mechanical behavior which enables high-throughput experiments and, in some cases, can also provide images of the indented area through scanning. Both indenting and scanning can cause tip wear that can influence the measurements. Therefore, precise characterization of tip radii is needed to improve data evaluation. A data fusion method is introduced which uses finite element simulations and experimental data to estimate the tip radius in situ in a meaningful way using an interpretable multi-fidelity deep learning approach. By interpreting the machine learning models, it is shown that the approaches are able to accurately capture physical indentation phenomena. |
format | Online Article Text |
id | pubmed-9122886 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-91228862022-05-22 Bridging Fidelities to Predict Nanoindentation Tip Radii Using Interpretable Deep Learning Models Trost, Claus O. W. Zak, Stanislav Schaffer, Sebastian Saringer, Christian Exl, Lukas Cordill, Megan J. JOM (1989) 30 Years of Oliver-Pharr: Then, Now and the Future of Nanoindentation As the need for miniaturized structural and functional materials has increased, the need for precise materials characterizaton has also expanded. Nanoindentation is a popular method that can be used to measure material mechanical behavior which enables high-throughput experiments and, in some cases, can also provide images of the indented area through scanning. Both indenting and scanning can cause tip wear that can influence the measurements. Therefore, precise characterization of tip radii is needed to improve data evaluation. A data fusion method is introduced which uses finite element simulations and experimental data to estimate the tip radius in situ in a meaningful way using an interpretable multi-fidelity deep learning approach. By interpreting the machine learning models, it is shown that the approaches are able to accurately capture physical indentation phenomena. Springer US 2022-04-01 2022 /pmc/articles/PMC9122886/ /pubmed/35611344 http://dx.doi.org/10.1007/s11837-022-05233-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | 30 Years of Oliver-Pharr: Then, Now and the Future of Nanoindentation Trost, Claus O. W. Zak, Stanislav Schaffer, Sebastian Saringer, Christian Exl, Lukas Cordill, Megan J. Bridging Fidelities to Predict Nanoindentation Tip Radii Using Interpretable Deep Learning Models |
title | Bridging Fidelities to Predict Nanoindentation Tip Radii Using Interpretable Deep Learning Models |
title_full | Bridging Fidelities to Predict Nanoindentation Tip Radii Using Interpretable Deep Learning Models |
title_fullStr | Bridging Fidelities to Predict Nanoindentation Tip Radii Using Interpretable Deep Learning Models |
title_full_unstemmed | Bridging Fidelities to Predict Nanoindentation Tip Radii Using Interpretable Deep Learning Models |
title_short | Bridging Fidelities to Predict Nanoindentation Tip Radii Using Interpretable Deep Learning Models |
title_sort | bridging fidelities to predict nanoindentation tip radii using interpretable deep learning models |
topic | 30 Years of Oliver-Pharr: Then, Now and the Future of Nanoindentation |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9122886/ https://www.ncbi.nlm.nih.gov/pubmed/35611344 http://dx.doi.org/10.1007/s11837-022-05233-z |
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