Pop-In Identification in Nanoindentation Curves with Deep Learning Algorithms

High–speed nanoindentation rapidly generates large datasets, opening the door for advanced data analysis methods such as the resources available in artificial intelligence. The present study addresses the problem of differentiating load–displacement curves presenting pop-in, slope changes, or instab...

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Autores principales:	Stephania Kossman, Maxence Bigerelle
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	nanoindentation pop-in artificial intelligence deep learning computer vision Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
Acceso en línea:	https://doaj.org/article/3010fedbaad04314993b940bd1bdd3cd
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spelling	oai:doaj.org-article:3010fedbaad04314993b940bd1bdd3cd2021-11-25T18:15:46ZPop-In Identification in Nanoindentation Curves with Deep Learning Algorithms10.3390/ma142270271996-1944https://doaj.org/article/3010fedbaad04314993b940bd1bdd3cd2021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1944/14/22/7027https://doaj.org/toc/1996-1944High–speed nanoindentation rapidly generates large datasets, opening the door for advanced data analysis methods such as the resources available in artificial intelligence. The present study addresses the problem of differentiating load–displacement curves presenting pop-in, slope changes, or instabilities from curves exhibiting a typical loading path in large nanoindentation datasets. Classification of the curves was achieved with a deep learning model, specifically, a convolutional neural network (CNN) model implemented in Python using TensorFlow and Keras libraries. Load–displacement curves (with pop-in and without pop-in) from various materials were input to train and validate the model. The curves were converted into square matrices (50 × 50) and then used as inputs for the CNN model. The model successfully differentiated between pop-in and non-pop-in curves with approximately 93% accuracy in the training and validation datasets, indicating that the risk of overfitting the model was negligible. These results confirmed that artificial intelligence and computer vision models represent a powerful tool for analyzing nanoindentation data.Stephania KossmanMaxence BigerelleMDPI AGarticlenanoindentationpop-inartificial intelligencedeep learningcomputer visionTechnologyTElectrical engineering. Electronics. Nuclear engineeringTK1-9971Engineering (General). Civil engineering (General)TA1-2040MicroscopyQH201-278.5Descriptive and experimental mechanicsQC120-168.85ENMaterials, Vol 14, Iss 7027, p 7027 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	nanoindentation pop-in artificial intelligence deep learning computer vision Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
spellingShingle	nanoindentation pop-in artificial intelligence deep learning computer vision Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85 Stephania Kossman Maxence Bigerelle Pop-In Identification in Nanoindentation Curves with Deep Learning Algorithms
description	High–speed nanoindentation rapidly generates large datasets, opening the door for advanced data analysis methods such as the resources available in artificial intelligence. The present study addresses the problem of differentiating load–displacement curves presenting pop-in, slope changes, or instabilities from curves exhibiting a typical loading path in large nanoindentation datasets. Classification of the curves was achieved with a deep learning model, specifically, a convolutional neural network (CNN) model implemented in Python using TensorFlow and Keras libraries. Load–displacement curves (with pop-in and without pop-in) from various materials were input to train and validate the model. The curves were converted into square matrices (50 × 50) and then used as inputs for the CNN model. The model successfully differentiated between pop-in and non-pop-in curves with approximately 93% accuracy in the training and validation datasets, indicating that the risk of overfitting the model was negligible. These results confirmed that artificial intelligence and computer vision models represent a powerful tool for analyzing nanoindentation data.
format	article
author	Stephania Kossman Maxence Bigerelle
author_facet	Stephania Kossman Maxence Bigerelle
author_sort	Stephania Kossman
title	Pop-In Identification in Nanoindentation Curves with Deep Learning Algorithms
title_short	Pop-In Identification in Nanoindentation Curves with Deep Learning Algorithms
title_full	Pop-In Identification in Nanoindentation Curves with Deep Learning Algorithms
title_fullStr	Pop-In Identification in Nanoindentation Curves with Deep Learning Algorithms
title_full_unstemmed	Pop-In Identification in Nanoindentation Curves with Deep Learning Algorithms
title_sort	pop-in identification in nanoindentation curves with deep learning algorithms
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3010fedbaad04314993b940bd1bdd3cd
work_keys_str_mv	AT stephaniakossman popinidentificationinnanoindentationcurveswithdeeplearningalgorithms AT maxencebigerelle popinidentificationinnanoindentationcurveswithdeeplearningalgorithms
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Pop-In Identification in Nanoindentation Curves with Deep Learning Algorithms

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