Characterize traction–separation relation and interfacial imperfections by data-driven machine learning models

Abstract Interfacial mechanical properties are important in composite materials and their applications, including vehicle structures, soft robotics, and aerospace. Determination of traction–separation (T–S) relations at interfaces in composites can lead to evaluations of structural reliability, mech...

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Autores principales:	Sanjida Ferdousi, Qiyi Chen, Mehrzad Soltani, Jiadeng Zhu, Pengfei Cao, Wonbong Choi, Rigoberto Advincula, Yijie Jiang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/81da60b87f8d424fb0603c028c7a809f
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spelling	oai:doaj.org-article:81da60b87f8d424fb0603c028c7a809f2021-12-02T16:08:08ZCharacterize traction–separation relation and interfacial imperfections by data-driven machine learning models10.1038/s41598-021-93852-y2045-2322https://doaj.org/article/81da60b87f8d424fb0603c028c7a809f2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93852-yhttps://doaj.org/toc/2045-2322Abstract Interfacial mechanical properties are important in composite materials and their applications, including vehicle structures, soft robotics, and aerospace. Determination of traction–separation (T–S) relations at interfaces in composites can lead to evaluations of structural reliability, mechanical robustness, and failures criteria. Accurate measurements on T–S relations remain challenging, since the interface interaction generally happens at microscale. With the emergence of machine learning (ML), data-driven model becomes an efficient method to predict the interfacial behaviors of composite materials and establish their mechanical models. Here, we combine ML, finite element analysis (FEA), and empirical experiments to develop data-driven models that characterize interfacial mechanical properties precisely. Specifically, eXtreme Gradient Boosting (XGBoost) multi-output regressions and classifier models are harnessed to investigate T–S relations and identify the imperfection locations at interface, respectively. The ML models are trained by macroscale force–displacement curves, which can be obtained from FEA and standard mechanical tests. The results show accurate predictions of T–S relations (R 2 = 0.988) and identification of imperfection locations with 81% accuracy. Our models are experimentally validated by 3D printed double cantilever beam specimens from different materials. Furthermore, we provide a code package containing trained ML models, allowing other researchers to establish T–S relations for different material interfaces.Sanjida FerdousiQiyi ChenMehrzad SoltaniJiadeng ZhuPengfei CaoWonbong ChoiRigoberto AdvinculaYijie JiangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sanjida Ferdousi Qiyi Chen Mehrzad Soltani Jiadeng Zhu Pengfei Cao Wonbong Choi Rigoberto Advincula Yijie Jiang Characterize traction–separation relation and interfacial imperfections by data-driven machine learning models
description	Abstract Interfacial mechanical properties are important in composite materials and their applications, including vehicle structures, soft robotics, and aerospace. Determination of traction–separation (T–S) relations at interfaces in composites can lead to evaluations of structural reliability, mechanical robustness, and failures criteria. Accurate measurements on T–S relations remain challenging, since the interface interaction generally happens at microscale. With the emergence of machine learning (ML), data-driven model becomes an efficient method to predict the interfacial behaviors of composite materials and establish their mechanical models. Here, we combine ML, finite element analysis (FEA), and empirical experiments to develop data-driven models that characterize interfacial mechanical properties precisely. Specifically, eXtreme Gradient Boosting (XGBoost) multi-output regressions and classifier models are harnessed to investigate T–S relations and identify the imperfection locations at interface, respectively. The ML models are trained by macroscale force–displacement curves, which can be obtained from FEA and standard mechanical tests. The results show accurate predictions of T–S relations (R 2 = 0.988) and identification of imperfection locations with 81% accuracy. Our models are experimentally validated by 3D printed double cantilever beam specimens from different materials. Furthermore, we provide a code package containing trained ML models, allowing other researchers to establish T–S relations for different material interfaces.
format	article
author	Sanjida Ferdousi Qiyi Chen Mehrzad Soltani Jiadeng Zhu Pengfei Cao Wonbong Choi Rigoberto Advincula Yijie Jiang
author_facet	Sanjida Ferdousi Qiyi Chen Mehrzad Soltani Jiadeng Zhu Pengfei Cao Wonbong Choi Rigoberto Advincula Yijie Jiang
author_sort	Sanjida Ferdousi
title	Characterize traction–separation relation and interfacial imperfections by data-driven machine learning models
title_short	Characterize traction–separation relation and interfacial imperfections by data-driven machine learning models
title_full	Characterize traction–separation relation and interfacial imperfections by data-driven machine learning models
title_fullStr	Characterize traction–separation relation and interfacial imperfections by data-driven machine learning models
title_full_unstemmed	Characterize traction–separation relation and interfacial imperfections by data-driven machine learning models
title_sort	characterize traction–separation relation and interfacial imperfections by data-driven machine learning models
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/81da60b87f8d424fb0603c028c7a809f
work_keys_str_mv	AT sanjidaferdousi characterizetractionseparationrelationandinterfacialimperfectionsbydatadrivenmachinelearningmodels AT qiyichen characterizetractionseparationrelationandinterfacialimperfectionsbydatadrivenmachinelearningmodels AT mehrzadsoltani characterizetractionseparationrelationandinterfacialimperfectionsbydatadrivenmachinelearningmodels AT jiadengzhu characterizetractionseparationrelationandinterfacialimperfectionsbydatadrivenmachinelearningmodels AT pengfeicao characterizetractionseparationrelationandinterfacialimperfectionsbydatadrivenmachinelearningmodels AT wonbongchoi characterizetractionseparationrelationandinterfacialimperfectionsbydatadrivenmachinelearningmodels AT rigobertoadvincula characterizetractionseparationrelationandinterfacialimperfectionsbydatadrivenmachinelearningmodels AT yijiejiang characterizetractionseparationrelationandinterfacialimperfectionsbydatadrivenmachinelearningmodels
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Characterize traction–separation relation and interfacial imperfections by data-driven machine learning models

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