A machine learning model for predicting the ballistic impact resistance of unidirectional fiber-reinforced composite plate

Abstract It has been a vital issue to ensure both the accuracy and efficiency of computational models for analyzing the ballistic impact response of fiber-reinforced composite plates (FRCP). In this paper, a machine learning (ML) model is established in an effort to bridge the ballistic impact prote...

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Autores principales:	X. D. Lei, X. Q. Wu, Z. Zhang, K. L. Xiao, Y. W. Wang, C. G. Huang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b2f73d076e954dd5aa787a3a37bacfde
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spelling	oai:doaj.org-article:b2f73d076e954dd5aa787a3a37bacfde2021-12-02T13:24:25ZA machine learning model for predicting the ballistic impact resistance of unidirectional fiber-reinforced composite plate10.1038/s41598-021-85963-32045-2322https://doaj.org/article/b2f73d076e954dd5aa787a3a37bacfde2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85963-3https://doaj.org/toc/2045-2322Abstract It has been a vital issue to ensure both the accuracy and efficiency of computational models for analyzing the ballistic impact response of fiber-reinforced composite plates (FRCP). In this paper, a machine learning (ML) model is established in an effort to bridge the ballistic impact protective performance and the characteristics of microstructure for unidirectional FRCP (UD-FRCP), where the microstructure of the UD-FRCP is characterized by the two-point correlation function. The results showed that the ML model, after trained by 175 cases, could reasonably predict the ballistic impact energy absorption of the UD-FRCP with a maximum error of 13%, indicating that the model can ensure both computational accuracy and efficiency. Besides, the model’s critical parameter sensitivities are investigated, and three typical ML algorithms are analyzed, showing that the gradient boosting regression algorithm has the highest accuracy among these algorithms for the ballistic impact problem of UD-FRCP. The study proposes an effective solution for the traditional difficulty of the ballistic impact simulation of composites with both high efficiency and accuracy.X. D. LeiX. Q. WuZ. ZhangK. L. XiaoY. W. WangC. G. HuangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q X. D. Lei X. Q. Wu Z. Zhang K. L. Xiao Y. W. Wang C. G. Huang A machine learning model for predicting the ballistic impact resistance of unidirectional fiber-reinforced composite plate
description	Abstract It has been a vital issue to ensure both the accuracy and efficiency of computational models for analyzing the ballistic impact response of fiber-reinforced composite plates (FRCP). In this paper, a machine learning (ML) model is established in an effort to bridge the ballistic impact protective performance and the characteristics of microstructure for unidirectional FRCP (UD-FRCP), where the microstructure of the UD-FRCP is characterized by the two-point correlation function. The results showed that the ML model, after trained by 175 cases, could reasonably predict the ballistic impact energy absorption of the UD-FRCP with a maximum error of 13%, indicating that the model can ensure both computational accuracy and efficiency. Besides, the model’s critical parameter sensitivities are investigated, and three typical ML algorithms are analyzed, showing that the gradient boosting regression algorithm has the highest accuracy among these algorithms for the ballistic impact problem of UD-FRCP. The study proposes an effective solution for the traditional difficulty of the ballistic impact simulation of composites with both high efficiency and accuracy.
format	article
author	X. D. Lei X. Q. Wu Z. Zhang K. L. Xiao Y. W. Wang C. G. Huang
author_facet	X. D. Lei X. Q. Wu Z. Zhang K. L. Xiao Y. W. Wang C. G. Huang
author_sort	X. D. Lei
title	A machine learning model for predicting the ballistic impact resistance of unidirectional fiber-reinforced composite plate
title_short	A machine learning model for predicting the ballistic impact resistance of unidirectional fiber-reinforced composite plate
title_full	A machine learning model for predicting the ballistic impact resistance of unidirectional fiber-reinforced composite plate
title_fullStr	A machine learning model for predicting the ballistic impact resistance of unidirectional fiber-reinforced composite plate
title_full_unstemmed	A machine learning model for predicting the ballistic impact resistance of unidirectional fiber-reinforced composite plate
title_sort	machine learning model for predicting the ballistic impact resistance of unidirectional fiber-reinforced composite plate
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b2f73d076e954dd5aa787a3a37bacfde
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A machine learning model for predicting the ballistic impact resistance of unidirectional fiber-reinforced composite plate

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