Detection of Material Degradation of a Composite Cylinder Using Mode Shapes and Convolutional Neural Networks

This paper presents a numerical study of the feasibility of using vibration mode shapes to identify material degradation in composite structures. The considered structure is a multilayer composite cylinder, while the material degradation zone is, for simplicity, considered a square section of the la...

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Autores principales:	Bartosz Miller, Leonard Ziemiański
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	shell layered composites mode shapes non-destructive tests machine learning 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/9349694966214601980bd33f5974278f
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spelling	oai:doaj.org-article:9349694966214601980bd33f5974278f2021-11-11T18:11:36ZDetection of Material Degradation of a Composite Cylinder Using Mode Shapes and Convolutional Neural Networks10.3390/ma142166861996-1944https://doaj.org/article/9349694966214601980bd33f5974278f2021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1944/14/21/6686https://doaj.org/toc/1996-1944This paper presents a numerical study of the feasibility of using vibration mode shapes to identify material degradation in composite structures. The considered structure is a multilayer composite cylinder, while the material degradation zone is, for simplicity, considered a square section of the lateral surface of the cylinder. The material degradation zone size and location along the cylinder axis are identified using a deep learning approach (convolutional neural networks, CNNs, are applied) on the basis of previously identified vibration mode shapes. The different numbers and combinations of identified mode shapes used to assess the damaged zone size and location were analyzed in detail. The final selection of mode shapes considered in the identification procedure yielded high accuracy in the identification of the degradation zone.Bartosz MillerLeonard ZiemiańskiMDPI AGarticleshelllayered compositesmode shapesnon-destructive testsmachine learningTechnologyTElectrical engineering. Electronics. Nuclear engineeringTK1-9971Engineering (General). Civil engineering (General)TA1-2040MicroscopyQH201-278.5Descriptive and experimental mechanicsQC120-168.85ENMaterials, Vol 14, Iss 6686, p 6686 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	shell layered composites mode shapes non-destructive tests machine learning 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	shell layered composites mode shapes non-destructive tests machine learning 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 Bartosz Miller Leonard Ziemiański Detection of Material Degradation of a Composite Cylinder Using Mode Shapes and Convolutional Neural Networks
description	This paper presents a numerical study of the feasibility of using vibration mode shapes to identify material degradation in composite structures. The considered structure is a multilayer composite cylinder, while the material degradation zone is, for simplicity, considered a square section of the lateral surface of the cylinder. The material degradation zone size and location along the cylinder axis are identified using a deep learning approach (convolutional neural networks, CNNs, are applied) on the basis of previously identified vibration mode shapes. The different numbers and combinations of identified mode shapes used to assess the damaged zone size and location were analyzed in detail. The final selection of mode shapes considered in the identification procedure yielded high accuracy in the identification of the degradation zone.
format	article
author	Bartosz Miller Leonard Ziemiański
author_facet	Bartosz Miller Leonard Ziemiański
author_sort	Bartosz Miller
title	Detection of Material Degradation of a Composite Cylinder Using Mode Shapes and Convolutional Neural Networks
title_short	Detection of Material Degradation of a Composite Cylinder Using Mode Shapes and Convolutional Neural Networks
title_full	Detection of Material Degradation of a Composite Cylinder Using Mode Shapes and Convolutional Neural Networks
title_fullStr	Detection of Material Degradation of a Composite Cylinder Using Mode Shapes and Convolutional Neural Networks
title_full_unstemmed	Detection of Material Degradation of a Composite Cylinder Using Mode Shapes and Convolutional Neural Networks
title_sort	detection of material degradation of a composite cylinder using mode shapes and convolutional neural networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/9349694966214601980bd33f5974278f
work_keys_str_mv	AT bartoszmiller detectionofmaterialdegradationofacompositecylinderusingmodeshapesandconvolutionalneuralnetworks AT leonardziemianski detectionofmaterialdegradationofacompositecylinderusingmodeshapesandconvolutionalneuralnetworks
_version_	1718431899525840896

Detection of Material Degradation of a Composite Cylinder Using Mode Shapes and Convolutional Neural Networks

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