Use of Artificial Neural Networks to Optimize Stacking Sequence in UHMWPE Protections

The aim of the present work is to provide a methodology to evaluate the influence of stacking sequence on the ballistic performance of ultra-high molecular weight polyethylene (UHMWPE) protections. The proposed methodology is based on the combination of experimental tests, numerical modelling, and A...
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Autor*in:	Jairo Peinado [verfasserIn] Liu Jiao-Wang [verfasserIn] Álvaro Olmedo [verfasserIn] Carlos Santiuste [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	UHMWPE impact FEM neural networks

Übergeordnetes Werk:	In: Polymers - MDPI AG, 2011, 13(2021), 7, p 1012
Übergeordnetes Werk:	volume:13 ; year:2021 ; number:7, p 1012

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/polym13071012

Katalog-ID:	DOAJ007874472

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spelling	10.3390/polym13071012 doi (DE-627)DOAJ007874472 (DE-599)DOAJef4cc15ce95e4bf599f54d1905cae5e9 DE-627 ger DE-627 rakwb eng QD241-441 Jairo Peinado verfasserin aut Use of Artificial Neural Networks to Optimize Stacking Sequence in UHMWPE Protections 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The aim of the present work is to provide a methodology to evaluate the influence of stacking sequence on the ballistic performance of ultra-high molecular weight polyethylene (UHMWPE) protections. The proposed methodology is based on the combination of experimental tests, numerical modelling, and Artificial Neural Networks (ANN). High-velocity impact experimental tests were conducted to validate the numerical model. The validated Finite Element Method (FEM) model was used to provide data to train and to validate the ANN. Finally, the ANN was used to find the best stacking sequence combining layers of three UHMWPE materials with different qualities. The results showed that the three UHMWPE materials can be properly combined to provide a solution with a better ballistic performance than using only the material with highest quality. These results imply that costs can be reduced increasing the ballistic limit of the UHMWPE protections. When the weight ratios of the three materials remain constant, the optimal results occur when the highest-performance material is placed in the back face. Furthermore, ANN simulation showed that the optimal results occur when the weight ratio of the highest-performance material is 79.2%. UHMWPE impact FEM neural networks Organic chemistry Liu Jiao-Wang verfasserin aut Álvaro Olmedo verfasserin aut Carlos Santiuste verfasserin aut In Polymers MDPI AG, 2011 13(2021), 7, p 1012 (DE-627)61409612X (DE-600)2527146-5 20734360 nnns volume:13 year:2021 number:7, p 1012 https://doi.org/10.3390/polym13071012 kostenfrei https://doaj.org/article/ef4cc15ce95e4bf599f54d1905cae5e9 kostenfrei https://www.mdpi.com/2073-4360/13/7/1012 kostenfrei https://doaj.org/toc/2073-4360 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 7, p 1012
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allfieldsGer	10.3390/polym13071012 doi (DE-627)DOAJ007874472 (DE-599)DOAJef4cc15ce95e4bf599f54d1905cae5e9 DE-627 ger DE-627 rakwb eng QD241-441 Jairo Peinado verfasserin aut Use of Artificial Neural Networks to Optimize Stacking Sequence in UHMWPE Protections 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The aim of the present work is to provide a methodology to evaluate the influence of stacking sequence on the ballistic performance of ultra-high molecular weight polyethylene (UHMWPE) protections. The proposed methodology is based on the combination of experimental tests, numerical modelling, and Artificial Neural Networks (ANN). High-velocity impact experimental tests were conducted to validate the numerical model. The validated Finite Element Method (FEM) model was used to provide data to train and to validate the ANN. Finally, the ANN was used to find the best stacking sequence combining layers of three UHMWPE materials with different qualities. The results showed that the three UHMWPE materials can be properly combined to provide a solution with a better ballistic performance than using only the material with highest quality. These results imply that costs can be reduced increasing the ballistic limit of the UHMWPE protections. When the weight ratios of the three materials remain constant, the optimal results occur when the highest-performance material is placed in the back face. Furthermore, ANN simulation showed that the optimal results occur when the weight ratio of the highest-performance material is 79.2%. UHMWPE impact FEM neural networks Organic chemistry Liu Jiao-Wang verfasserin aut Álvaro Olmedo verfasserin aut Carlos Santiuste verfasserin aut In Polymers MDPI AG, 2011 13(2021), 7, p 1012 (DE-627)61409612X (DE-600)2527146-5 20734360 nnns volume:13 year:2021 number:7, p 1012 https://doi.org/10.3390/polym13071012 kostenfrei https://doaj.org/article/ef4cc15ce95e4bf599f54d1905cae5e9 kostenfrei https://www.mdpi.com/2073-4360/13/7/1012 kostenfrei https://doaj.org/toc/2073-4360 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 7, p 1012
allfieldsSound	10.3390/polym13071012 doi (DE-627)DOAJ007874472 (DE-599)DOAJef4cc15ce95e4bf599f54d1905cae5e9 DE-627 ger DE-627 rakwb eng QD241-441 Jairo Peinado verfasserin aut Use of Artificial Neural Networks to Optimize Stacking Sequence in UHMWPE Protections 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The aim of the present work is to provide a methodology to evaluate the influence of stacking sequence on the ballistic performance of ultra-high molecular weight polyethylene (UHMWPE) protections. The proposed methodology is based on the combination of experimental tests, numerical modelling, and Artificial Neural Networks (ANN). High-velocity impact experimental tests were conducted to validate the numerical model. The validated Finite Element Method (FEM) model was used to provide data to train and to validate the ANN. Finally, the ANN was used to find the best stacking sequence combining layers of three UHMWPE materials with different qualities. The results showed that the three UHMWPE materials can be properly combined to provide a solution with a better ballistic performance than using only the material with highest quality. These results imply that costs can be reduced increasing the ballistic limit of the UHMWPE protections. When the weight ratios of the three materials remain constant, the optimal results occur when the highest-performance material is placed in the back face. Furthermore, ANN simulation showed that the optimal results occur when the weight ratio of the highest-performance material is 79.2%. UHMWPE impact FEM neural networks Organic chemistry Liu Jiao-Wang verfasserin aut Álvaro Olmedo verfasserin aut Carlos Santiuste verfasserin aut In Polymers MDPI AG, 2011 13(2021), 7, p 1012 (DE-627)61409612X (DE-600)2527146-5 20734360 nnns volume:13 year:2021 number:7, p 1012 https://doi.org/10.3390/polym13071012 kostenfrei https://doaj.org/article/ef4cc15ce95e4bf599f54d1905cae5e9 kostenfrei https://www.mdpi.com/2073-4360/13/7/1012 kostenfrei https://doaj.org/toc/2073-4360 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 7, p 1012
language	English
source	In Polymers 13(2021), 7, p 1012 volume:13 year:2021 number:7, p 1012
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format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	UHMWPE impact FEM neural networks Organic chemistry
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authorswithroles_txt_mv	Jairo Peinado @@aut@@ Liu Jiao-Wang @@aut@@ Álvaro Olmedo @@aut@@ Carlos Santiuste @@aut@@
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callnumber-first	Q - Science
author	Jairo Peinado
spellingShingle	Jairo Peinado misc QD241-441 misc UHMWPE misc impact misc FEM misc neural networks misc Organic chemistry Use of Artificial Neural Networks to Optimize Stacking Sequence in UHMWPE Protections
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topic_title	QD241-441 Use of Artificial Neural Networks to Optimize Stacking Sequence in UHMWPE Protections UHMWPE impact FEM neural networks
topic	misc QD241-441 misc UHMWPE misc impact misc FEM misc neural networks misc Organic chemistry
topic_unstemmed	misc QD241-441 misc UHMWPE misc impact misc FEM misc neural networks misc Organic chemistry
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title	Use of Artificial Neural Networks to Optimize Stacking Sequence in UHMWPE Protections
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title_full	Use of Artificial Neural Networks to Optimize Stacking Sequence in UHMWPE Protections
author_sort	Jairo Peinado
journal	Polymers
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author_browse	Jairo Peinado Liu Jiao-Wang Álvaro Olmedo Carlos Santiuste
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title_sort	use of artificial neural networks to optimize stacking sequence in uhmwpe protections
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title_auth	Use of Artificial Neural Networks to Optimize Stacking Sequence in UHMWPE Protections
abstract	The aim of the present work is to provide a methodology to evaluate the influence of stacking sequence on the ballistic performance of ultra-high molecular weight polyethylene (UHMWPE) protections. The proposed methodology is based on the combination of experimental tests, numerical modelling, and Artificial Neural Networks (ANN). High-velocity impact experimental tests were conducted to validate the numerical model. The validated Finite Element Method (FEM) model was used to provide data to train and to validate the ANN. Finally, the ANN was used to find the best stacking sequence combining layers of three UHMWPE materials with different qualities. The results showed that the three UHMWPE materials can be properly combined to provide a solution with a better ballistic performance than using only the material with highest quality. These results imply that costs can be reduced increasing the ballistic limit of the UHMWPE protections. When the weight ratios of the three materials remain constant, the optimal results occur when the highest-performance material is placed in the back face. Furthermore, ANN simulation showed that the optimal results occur when the weight ratio of the highest-performance material is 79.2%.
abstractGer	The aim of the present work is to provide a methodology to evaluate the influence of stacking sequence on the ballistic performance of ultra-high molecular weight polyethylene (UHMWPE) protections. The proposed methodology is based on the combination of experimental tests, numerical modelling, and Artificial Neural Networks (ANN). High-velocity impact experimental tests were conducted to validate the numerical model. The validated Finite Element Method (FEM) model was used to provide data to train and to validate the ANN. Finally, the ANN was used to find the best stacking sequence combining layers of three UHMWPE materials with different qualities. The results showed that the three UHMWPE materials can be properly combined to provide a solution with a better ballistic performance than using only the material with highest quality. These results imply that costs can be reduced increasing the ballistic limit of the UHMWPE protections. When the weight ratios of the three materials remain constant, the optimal results occur when the highest-performance material is placed in the back face. Furthermore, ANN simulation showed that the optimal results occur when the weight ratio of the highest-performance material is 79.2%.
abstract_unstemmed	The aim of the present work is to provide a methodology to evaluate the influence of stacking sequence on the ballistic performance of ultra-high molecular weight polyethylene (UHMWPE) protections. The proposed methodology is based on the combination of experimental tests, numerical modelling, and Artificial Neural Networks (ANN). High-velocity impact experimental tests were conducted to validate the numerical model. The validated Finite Element Method (FEM) model was used to provide data to train and to validate the ANN. Finally, the ANN was used to find the best stacking sequence combining layers of three UHMWPE materials with different qualities. The results showed that the three UHMWPE materials can be properly combined to provide a solution with a better ballistic performance than using only the material with highest quality. These results imply that costs can be reduced increasing the ballistic limit of the UHMWPE protections. When the weight ratios of the three materials remain constant, the optimal results occur when the highest-performance material is placed in the back face. Furthermore, ANN simulation showed that the optimal results occur when the weight ratio of the highest-performance material is 79.2%.
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container_issue	7, p 1012
title_short	Use of Artificial Neural Networks to Optimize Stacking Sequence in UHMWPE Protections
url	https://doi.org/10.3390/polym13071012 https://doaj.org/article/ef4cc15ce95e4bf599f54d1905cae5e9 https://www.mdpi.com/2073-4360/13/7/1012 https://doaj.org/toc/2073-4360
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author2	Liu Jiao-Wang Álvaro Olmedo Carlos Santiuste
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