Artificial Neural Networks for Producing a Low-Cost Austempered Ductile Iron

Detalhes bibliográficos
Autor(a) principal: Hofmam,Diogo
Data de Publicação: 2022
Outros Autores: Ramos,Fabiano Dornelles, Lemos,Guilherme Vieira Braga, Lessa,Cleber Rodrigo de Lima
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Materials research (São Carlos. Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-14392022000100391
Resumo: Two artificial neural networks (ANNs) were developed for producing an austempered ductile iron (ADI) with low-cost chemical composition and mechanical properties as per ASTMA897/897M-16-grade-1050/750/07 standard. Thus, the first ANN predicted the chemical composition range within the lowest cost and required mechanical properties. Next, in the second ANN, the resulting values from the first ANN were refined considering the target chemical composition suggested in the standard. Moreover, mechanical properties and microstructural analyses were undertaken in the ADI produced to support the ANNs’ findings. Hence, ANNs can be used to make a standard-compliant ADI and achieve cost savings.
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spelling Artificial Neural Networks for Producing a Low-Cost Austempered Ductile IronAustempered ductile ironArtificial neural networkMechanical propertiesCost-savingsTwo artificial neural networks (ANNs) were developed for producing an austempered ductile iron (ADI) with low-cost chemical composition and mechanical properties as per ASTMA897/897M-16-grade-1050/750/07 standard. Thus, the first ANN predicted the chemical composition range within the lowest cost and required mechanical properties. Next, in the second ANN, the resulting values from the first ANN were refined considering the target chemical composition suggested in the standard. Moreover, mechanical properties and microstructural analyses were undertaken in the ADI produced to support the ANNs’ findings. Hence, ANNs can be used to make a standard-compliant ADI and achieve cost savings.ABM, ABC, ABPol2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-14392022000100391Materials Research v.25 2022reponame:Materials research (São Carlos. Online)instname:Universidade Federal de São Carlos (UFSCAR)instacron:ABM ABC ABPOL10.1590/1980-5373-mr-2022-0336info:eu-repo/semantics/openAccessHofmam,DiogoRamos,Fabiano DornellesLemos,Guilherme Vieira BragaLessa,Cleber Rodrigo de Limaeng2022-11-16T00:00:00Zoai:scielo:S1516-14392022000100391Revistahttp://www.scielo.br/mrPUBhttps://old.scielo.br/oai/scielo-oai.phpdedz@power.ufscar.br1980-53731516-1439opendoar:2022-11-16T00:00Materials research (São Carlos. Online) - Universidade Federal de São Carlos (UFSCAR)false
dc.title.none.fl_str_mv Artificial Neural Networks for Producing a Low-Cost Austempered Ductile Iron
title Artificial Neural Networks for Producing a Low-Cost Austempered Ductile Iron
spellingShingle Artificial Neural Networks for Producing a Low-Cost Austempered Ductile Iron
Hofmam,Diogo
Austempered ductile iron
Artificial neural network
Mechanical properties
Cost-savings
title_short Artificial Neural Networks for Producing a Low-Cost Austempered Ductile Iron
title_full Artificial Neural Networks for Producing a Low-Cost Austempered Ductile Iron
title_fullStr Artificial Neural Networks for Producing a Low-Cost Austempered Ductile Iron
title_full_unstemmed Artificial Neural Networks for Producing a Low-Cost Austempered Ductile Iron
title_sort Artificial Neural Networks for Producing a Low-Cost Austempered Ductile Iron
author Hofmam,Diogo
author_facet Hofmam,Diogo
Ramos,Fabiano Dornelles
Lemos,Guilherme Vieira Braga
Lessa,Cleber Rodrigo de Lima
author_role author
author2 Ramos,Fabiano Dornelles
Lemos,Guilherme Vieira Braga
Lessa,Cleber Rodrigo de Lima
author2_role author
author
author
dc.contributor.author.fl_str_mv Hofmam,Diogo
Ramos,Fabiano Dornelles
Lemos,Guilherme Vieira Braga
Lessa,Cleber Rodrigo de Lima
dc.subject.por.fl_str_mv Austempered ductile iron
Artificial neural network
Mechanical properties
Cost-savings
topic Austempered ductile iron
Artificial neural network
Mechanical properties
Cost-savings
description Two artificial neural networks (ANNs) were developed for producing an austempered ductile iron (ADI) with low-cost chemical composition and mechanical properties as per ASTMA897/897M-16-grade-1050/750/07 standard. Thus, the first ANN predicted the chemical composition range within the lowest cost and required mechanical properties. Next, in the second ANN, the resulting values from the first ANN were refined considering the target chemical composition suggested in the standard. Moreover, mechanical properties and microstructural analyses were undertaken in the ADI produced to support the ANNs’ findings. Hence, ANNs can be used to make a standard-compliant ADI and achieve cost savings.
publishDate 2022
dc.date.none.fl_str_mv 2022-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-14392022000100391
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-14392022000100391
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/1980-5373-mr-2022-0336
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv ABM, ABC, ABPol
publisher.none.fl_str_mv ABM, ABC, ABPol
dc.source.none.fl_str_mv Materials Research v.25 2022
reponame:Materials research (São Carlos. Online)
instname:Universidade Federal de São Carlos (UFSCAR)
instacron:ABM ABC ABPOL
instname_str Universidade Federal de São Carlos (UFSCAR)
instacron_str ABM ABC ABPOL
institution ABM ABC ABPOL
reponame_str Materials research (São Carlos. Online)
collection Materials research (São Carlos. Online)
repository.name.fl_str_mv Materials research (São Carlos. Online) - Universidade Federal de São Carlos (UFSCAR)
repository.mail.fl_str_mv dedz@power.ufscar.br
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