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An Investigation of Nanomechanical Properties of Materials using Nanoindentation and Artificial Neural Network
Mechanical properties of materials can be derived from the force-displacement relationship through instrumented indentation tests. Complications arise when establishing the full elastic-plastic stress-strain relationship as the accuracy depends on how the material’s and indenter’s parameters are inc...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6742636/ https://www.ncbi.nlm.nih.gov/pubmed/31515524 http://dx.doi.org/10.1038/s41598-019-49780-z |
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author | Lee, Hyuk Huen, Wai Yeong Vimonsatit, Vanissorn Mendis, Priyan |
author_facet | Lee, Hyuk Huen, Wai Yeong Vimonsatit, Vanissorn Mendis, Priyan |
author_sort | Lee, Hyuk |
collection | PubMed |
description | Mechanical properties of materials can be derived from the force-displacement relationship through instrumented indentation tests. Complications arise when establishing the full elastic-plastic stress-strain relationship as the accuracy depends on how the material’s and indenter’s parameters are incorporated. For instance, the effect of the material work-hardening phenomenon such as the pile-up and sink-in effect cannot be accounted for with simplified analytical indentation solutions. Due to this limitation, this paper proposes a new inverse analysis approach based on dimensional functions analysis and artificial neural networks (ANNs). A database of the dimensional functions relating stress and strain parameters of materials has been developed. The database covers a wide range of engineering materials that have the yield strength-to-modulus ratio (σ(y)/E) between 0.001 to 0.5, the work-hardening power (n) between 0–0.5, Poisson’s ratio (v) between 0.15–0.45, and the indentation angle (θ) between 65–80 degrees. The proposed algorithm enables determining the nanomechanical stress-strain parameters using the indentation force-displacement relationship, and is applicable to any materials that the properties are within the database range. The obtained results are validated with the conventional test results of steel and aluminum samples. To further demonstrate the application of the proposed algorithm, the nanomechanical stress-strain parameters of ordinary Portland cement phases were determined. |
format | Online Article Text |
id | pubmed-6742636 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-67426362019-09-26 An Investigation of Nanomechanical Properties of Materials using Nanoindentation and Artificial Neural Network Lee, Hyuk Huen, Wai Yeong Vimonsatit, Vanissorn Mendis, Priyan Sci Rep Article Mechanical properties of materials can be derived from the force-displacement relationship through instrumented indentation tests. Complications arise when establishing the full elastic-plastic stress-strain relationship as the accuracy depends on how the material’s and indenter’s parameters are incorporated. For instance, the effect of the material work-hardening phenomenon such as the pile-up and sink-in effect cannot be accounted for with simplified analytical indentation solutions. Due to this limitation, this paper proposes a new inverse analysis approach based on dimensional functions analysis and artificial neural networks (ANNs). A database of the dimensional functions relating stress and strain parameters of materials has been developed. The database covers a wide range of engineering materials that have the yield strength-to-modulus ratio (σ(y)/E) between 0.001 to 0.5, the work-hardening power (n) between 0–0.5, Poisson’s ratio (v) between 0.15–0.45, and the indentation angle (θ) between 65–80 degrees. The proposed algorithm enables determining the nanomechanical stress-strain parameters using the indentation force-displacement relationship, and is applicable to any materials that the properties are within the database range. The obtained results are validated with the conventional test results of steel and aluminum samples. To further demonstrate the application of the proposed algorithm, the nanomechanical stress-strain parameters of ordinary Portland cement phases were determined. Nature Publishing Group UK 2019-09-12 /pmc/articles/PMC6742636/ /pubmed/31515524 http://dx.doi.org/10.1038/s41598-019-49780-z Text en © The Author(s) 2019 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Lee, Hyuk Huen, Wai Yeong Vimonsatit, Vanissorn Mendis, Priyan An Investigation of Nanomechanical Properties of Materials using Nanoindentation and Artificial Neural Network |
title | An Investigation of Nanomechanical Properties of Materials using Nanoindentation and Artificial Neural Network |
title_full | An Investigation of Nanomechanical Properties of Materials using Nanoindentation and Artificial Neural Network |
title_fullStr | An Investigation of Nanomechanical Properties of Materials using Nanoindentation and Artificial Neural Network |
title_full_unstemmed | An Investigation of Nanomechanical Properties of Materials using Nanoindentation and Artificial Neural Network |
title_short | An Investigation of Nanomechanical Properties of Materials using Nanoindentation and Artificial Neural Network |
title_sort | investigation of nanomechanical properties of materials using nanoindentation and artificial neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6742636/ https://www.ncbi.nlm.nih.gov/pubmed/31515524 http://dx.doi.org/10.1038/s41598-019-49780-z |
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