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...

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Autores principales:	C. Muir, B. Swaminathan, A. S. Almansour, K. Sevener, C. Smith, M. Presby, J. D. Kiser, T. M. Pollock, S. Daly
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/5a391a082cb849568002aaccc64712ce
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spelling	oai:doaj.org-article:5a391a082cb849568002aaccc64712ce2021-12-02T17:12:24ZDamage mechanism identification in composites via machine learning and acoustic emission10.1038/s41524-021-00565-x2057-3960https://doaj.org/article/5a391a082cb849568002aaccc64712ce2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00565-xhttps://doaj.org/toc/2057-3960Abstract 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.C. MuirB. SwaminathanA. S. AlmansourK. SevenerC. SmithM. PresbyJ. D. KiserT. M. PollockS. DalyNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 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
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.
format	article
author	C. Muir B. Swaminathan A. S. Almansour K. Sevener C. Smith M. Presby J. D. Kiser T. M. Pollock S. Daly
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 Portfolio
publishDate	2021
url	https://doaj.org/article/5a391a082cb849568002aaccc64712ce
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_	1718381379643768832

Damage mechanism identification in composites via machine learning and acoustic emission

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