An Investigation of Nanomechanical Properties of Materials using Nanoindentation and Artificial Neural Network

Abstract 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 parameter...

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Autores principales:	Hyuk Lee, Wai Yeong Huen, Vanissorn Vimonsatit, Priyan Mendis
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/88e65587803f401da61c9eb3bce297e6
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spelling	oai:doaj.org-article:88e65587803f401da61c9eb3bce297e62021-12-02T15:09:15ZAn Investigation of Nanomechanical Properties of Materials using Nanoindentation and Artificial Neural Network10.1038/s41598-019-49780-z2045-2322https://doaj.org/article/88e65587803f401da61c9eb3bce297e62019-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-49780-zhttps://doaj.org/toc/2045-2322Abstract 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.Hyuk LeeWai Yeong HuenVanissorn VimonsatitPriyan MendisNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-9 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hyuk Lee Wai Yeong Huen Vanissorn Vimonsatit Priyan Mendis An Investigation of Nanomechanical Properties of Materials using Nanoindentation and Artificial Neural Network
description	Abstract 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	article
author	Hyuk Lee Wai Yeong Huen Vanissorn Vimonsatit Priyan Mendis
author_facet	Hyuk Lee Wai Yeong Huen Vanissorn Vimonsatit Priyan Mendis
author_sort	Hyuk Lee
title	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_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_sort	investigation of nanomechanical properties of materials using nanoindentation and artificial neural network
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/88e65587803f401da61c9eb3bce297e6
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An Investigation of Nanomechanical Properties of Materials using Nanoindentation and Artificial Neural Network

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