Development of an artificial neural network for predicting energy absorption capability of thermoplastic commingled composites
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.compstruct.2020.113131 http://hdl.handle.net/11449/209850 |
Resumo: | Soft computing techniques including artificial neural networks (ANN) and machine learning reflect new possibilities to behavior prediction models of commingled composites. This study focuses on developing an artificial neural network capable of predicting the impact energy absorption capability of thermoplastic commingled composites, in the context of crashworthiness, based on a compilation of experimental results, multiple regression analytical model and factorial design method. Furthermore, the scientific approach of this project comprises the (i) development of intelligent models for designing and manufacturing of new composite components, (ii) application of computational methods to predict material performance and behavior, and (iii) optimization of manufacturing processes. The innovativeness of this proposal is to initiate the use of computational methods to describe mechanical and structural properties of thermoplastic commingled composite materials and the development of an artificial neural network able to predict the energy absorption capability of these materials, considering some properties of polymer matrix, thermal degradation kinetics model and consolidation parameters. The obtained results from impact testing indicate that the proposed approach can predict the impact energy with satisfactory accuracy. The use of an analytical model database as input for the ANN is an innovative methodology to increase the reliability and accuracy of the ANNs. |
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Development of an artificial neural network for predicting energy absorption capability of thermoplastic commingled compositesDesign of experimentsCommingled compositesCrashworthinessThermal degradation kineticsMultiple regression modelSoft computing techniques including artificial neural networks (ANN) and machine learning reflect new possibilities to behavior prediction models of commingled composites. This study focuses on developing an artificial neural network capable of predicting the impact energy absorption capability of thermoplastic commingled composites, in the context of crashworthiness, based on a compilation of experimental results, multiple regression analytical model and factorial design method. Furthermore, the scientific approach of this project comprises the (i) development of intelligent models for designing and manufacturing of new composite components, (ii) application of computational methods to predict material performance and behavior, and (iii) optimization of manufacturing processes. The innovativeness of this proposal is to initiate the use of computational methods to describe mechanical and structural properties of thermoplastic commingled composite materials and the development of an artificial neural network able to predict the energy absorption capability of these materials, considering some properties of polymer matrix, thermal degradation kinetics model and consolidation parameters. The obtained results from impact testing indicate that the proposed approach can predict the impact energy with satisfactory accuracy. The use of an analytical model database as input for the ANN is an innovative methodology to increase the reliability and accuracy of the ANNs.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)FINEPFundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)NSF Early Career AwardSao Paulo State Univ UNESP, Sch Engn, Mat & Technol Dept, Av Ariberto Pereira da Cunha 333, BR-333 Guaratingueta, SP, BrazilUniv Delaware UDEL, Dept Mat Sci & Engn, 212 DuPont Hall, Newark, DE 19716 USAFed Univ Itajuba UNIFEI, NTC Composite Technol Ctr, Mech Engn Inst, Av BPS, BR-1303 Itajuba, MG, BrazilSao Paulo State Univ UNESP, Sch Engn, Mat & Technol Dept, Av Ariberto Pereira da Cunha 333, BR-333 Guaratingueta, SP, BrazilFAPESP: 2018/24964-2FAPESP: 2019/22173-0FAPESP: 2017/16970-0CNPq: 303224/2016-9CNPq: 311709/2017-6FINEP: 0.1.13.0169.00FAPEMIG: APQ-00385-18FAPEMIG: APQ-0184618NSF Early Career Award: DMR-1652994Elsevier B.V.Universidade Estadual Paulista (Unesp)Univ Delaware UDELFed Univ Itajuba UNIFEIDi Benedetto, R. M. [UNESP]Botelho, E. C. [UNESP]Janotti, A.Ancelotti Junior, A. C.Gomes, G. F.2021-06-25T12:31:23Z2021-06-25T12:31:23Z2021-02-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article12http://dx.doi.org/10.1016/j.compstruct.2020.113131Composite Structures. Oxford: Elsevier Sci Ltd, v. 257, 12 p., 2021.0263-8223http://hdl.handle.net/11449/20985010.1016/j.compstruct.2020.113131WOS:000604730100003Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengComposite Structuresinfo:eu-repo/semantics/openAccess2024-07-02T15:03:45Zoai:repositorio.unesp.br:11449/209850Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:32:45.267136Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Development of an artificial neural network for predicting energy absorption capability of thermoplastic commingled composites |
title |
Development of an artificial neural network for predicting energy absorption capability of thermoplastic commingled composites |
spellingShingle |
Development of an artificial neural network for predicting energy absorption capability of thermoplastic commingled composites Di Benedetto, R. M. [UNESP] Design of experiments Commingled composites Crashworthiness Thermal degradation kinetics Multiple regression model |
title_short |
Development of an artificial neural network for predicting energy absorption capability of thermoplastic commingled composites |
title_full |
Development of an artificial neural network for predicting energy absorption capability of thermoplastic commingled composites |
title_fullStr |
Development of an artificial neural network for predicting energy absorption capability of thermoplastic commingled composites |
title_full_unstemmed |
Development of an artificial neural network for predicting energy absorption capability of thermoplastic commingled composites |
title_sort |
Development of an artificial neural network for predicting energy absorption capability of thermoplastic commingled composites |
author |
Di Benedetto, R. M. [UNESP] |
author_facet |
Di Benedetto, R. M. [UNESP] Botelho, E. C. [UNESP] Janotti, A. Ancelotti Junior, A. C. Gomes, G. F. |
author_role |
author |
author2 |
Botelho, E. C. [UNESP] Janotti, A. Ancelotti Junior, A. C. Gomes, G. F. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Univ Delaware UDEL Fed Univ Itajuba UNIFEI |
dc.contributor.author.fl_str_mv |
Di Benedetto, R. M. [UNESP] Botelho, E. C. [UNESP] Janotti, A. Ancelotti Junior, A. C. Gomes, G. F. |
dc.subject.por.fl_str_mv |
Design of experiments Commingled composites Crashworthiness Thermal degradation kinetics Multiple regression model |
topic |
Design of experiments Commingled composites Crashworthiness Thermal degradation kinetics Multiple regression model |
description |
Soft computing techniques including artificial neural networks (ANN) and machine learning reflect new possibilities to behavior prediction models of commingled composites. This study focuses on developing an artificial neural network capable of predicting the impact energy absorption capability of thermoplastic commingled composites, in the context of crashworthiness, based on a compilation of experimental results, multiple regression analytical model and factorial design method. Furthermore, the scientific approach of this project comprises the (i) development of intelligent models for designing and manufacturing of new composite components, (ii) application of computational methods to predict material performance and behavior, and (iii) optimization of manufacturing processes. The innovativeness of this proposal is to initiate the use of computational methods to describe mechanical and structural properties of thermoplastic commingled composite materials and the development of an artificial neural network able to predict the energy absorption capability of these materials, considering some properties of polymer matrix, thermal degradation kinetics model and consolidation parameters. The obtained results from impact testing indicate that the proposed approach can predict the impact energy with satisfactory accuracy. The use of an analytical model database as input for the ANN is an innovative methodology to increase the reliability and accuracy of the ANNs. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-06-25T12:31:23Z 2021-06-25T12:31:23Z 2021-02-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.compstruct.2020.113131 Composite Structures. Oxford: Elsevier Sci Ltd, v. 257, 12 p., 2021. 0263-8223 http://hdl.handle.net/11449/209850 10.1016/j.compstruct.2020.113131 WOS:000604730100003 |
url |
http://dx.doi.org/10.1016/j.compstruct.2020.113131 http://hdl.handle.net/11449/209850 |
identifier_str_mv |
Composite Structures. Oxford: Elsevier Sci Ltd, v. 257, 12 p., 2021. 0263-8223 10.1016/j.compstruct.2020.113131 WOS:000604730100003 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Composite Structures |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
12 |
dc.publisher.none.fl_str_mv |
Elsevier B.V. |
publisher.none.fl_str_mv |
Elsevier B.V. |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128669190717440 |