Damage mechanism identification in composites via machine learning and acoustic emission

Abstract Damage mechanism identification has scientific and practical ramifications for the structural health monitoring, design, and application of composite systems. Recent advances in machine learning uncover pathways to identify the waveform-damage mechanism relationship in higher-dimensional sp...

Full description

Bibliographic Details
Main Authors:	C. Muir, B. Swaminathan, A. S. Almansour, K. Sevener, C. Smith, M. Presby, J. D. Kiser, T. M. Pollock, S. Daly
Format:	Article
Language:	English
Published:	Nature Publishing Group 2021-06-01
Series:	npj Computational Materials
Online Access:	https://doi.org/10.1038/s41524-021-00565-x

id	doaj-5a391a082cb849568002aaccc64712ce
record_format	Article
spelling	doaj-5a391a082cb849568002aaccc64712ce2021-06-27T11:19:30ZengNature Publishing Groupnpj Computational Materials2057-39602021-06-017111510.1038/s41524-021-00565-xDamage mechanism identification in composites via machine learning and acoustic emissionC. Muir0B. Swaminathan1A. S. Almansour2K. Sevener3C. Smith4M. Presby5J. D. Kiser6T. M. Pollock7S. Daly8Materials Department, University of California-Santa BarbaraMaterials Department, University of California-Santa BarbaraNASA Glenn Research CenterMaterials Science and Engineering Department, University of Michigan-Ann ArborNASA Glenn Research CenterNASA Glenn Research CenterNASA Glenn Research CenterMaterials Department, University of California-Santa BarbaraMechanical Engineering Department, University of California-Santa BarbaraAbstract Damage mechanism identification has scientific and practical ramifications for the structural health monitoring, design, and application of composite systems. Recent advances in machine learning uncover pathways to identify the waveform-damage mechanism relationship in higher-dimensional spaces for a comprehensive understanding of damage evolution. This review evaluates the state of the field, beginning with a physics-based understanding of acoustic emission waveform feature extraction, followed by a detailed overview of waveform clustering, labeling, and error analysis strategies. Fundamental requirements for damage mechanism identification in any machine learning framework, including those currently in use, under development, and yet to be explored, are discussed.https://doi.org/10.1038/s41524-021-00565-x
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	C. Muir B. Swaminathan A. S. Almansour K. Sevener C. Smith M. Presby J. D. Kiser T. M. Pollock S. Daly
spellingShingle	C. Muir B. Swaminathan A. S. Almansour K. Sevener C. Smith M. Presby J. D. Kiser T. M. Pollock S. Daly Damage mechanism identification in composites via machine learning and acoustic emission npj Computational Materials
author_facet	C. Muir B. Swaminathan A. S. Almansour K. Sevener C. Smith M. Presby J. D. Kiser T. M. Pollock S. Daly
author_sort	C. Muir
title	Damage mechanism identification in composites via machine learning and acoustic emission
title_short	Damage mechanism identification in composites via machine learning and acoustic emission
title_full	Damage mechanism identification in composites via machine learning and acoustic emission
title_fullStr	Damage mechanism identification in composites via machine learning and acoustic emission
title_full_unstemmed	Damage mechanism identification in composites via machine learning and acoustic emission
title_sort	damage mechanism identification in composites via machine learning and acoustic emission
publisher	Nature Publishing Group
series	npj Computational Materials
issn	2057-3960
publishDate	2021-06-01
description	Abstract Damage mechanism identification has scientific and practical ramifications for the structural health monitoring, design, and application of composite systems. Recent advances in machine learning uncover pathways to identify the waveform-damage mechanism relationship in higher-dimensional spaces for a comprehensive understanding of damage evolution. This review evaluates the state of the field, beginning with a physics-based understanding of acoustic emission waveform feature extraction, followed by a detailed overview of waveform clustering, labeling, and error analysis strategies. Fundamental requirements for damage mechanism identification in any machine learning framework, including those currently in use, under development, and yet to be explored, are discussed.
url	https://doi.org/10.1038/s41524-021-00565-x
work_keys_str_mv	AT cmuir damagemechanismidentificationincompositesviamachinelearningandacousticemission AT bswaminathan damagemechanismidentificationincompositesviamachinelearningandacousticemission AT asalmansour damagemechanismidentificationincompositesviamachinelearningandacousticemission AT ksevener damagemechanismidentificationincompositesviamachinelearningandacousticemission AT csmith damagemechanismidentificationincompositesviamachinelearningandacousticemission AT mpresby damagemechanismidentificationincompositesviamachinelearningandacousticemission AT jdkiser damagemechanismidentificationincompositesviamachinelearningandacousticemission AT tmpollock damagemechanismidentificationincompositesviamachinelearningandacousticemission AT sdaly damagemechanismidentificationincompositesviamachinelearningandacousticemission
_version_	1721358000111747072

Damage mechanism identification in composites via machine learning and acoustic emission

Similar Items