Artificial Neural Network for Predicting Hardness of Multistage Solutionized and Artificially Aged LM4 + TiB2 Composites
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , |
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=S1516-14392022000100259 |
Resumo: | Abstract Aluminium casting alloy LM4 (EN 1706 AC-45200) composites with TiB2 (1, 2, and 3 wt.%) as reinforcements were produced using the two-stage stir casting method. OM and SEM study shows uniform and homogeneous reinforcement distribution in LM4 + TiB2 composites. As-cast composites were subjected to single-stage solution treatment at 520°C for 2 h and multistage solution treatment at 495 and 520°C for 2 and 4 h, followed by hot water quenching at 60°C and aging at 100 and 200°C for different time intervals. The hardness of as-cast and artificially aged composites were compared in both conditions. Compared to as-cast LM4 alloy, 20-45% improvement in hardness was observed for LM4 + TiB2 as-cast composites. 60-150% improvement in hardness was observed in artificially aged LM4 + 3 wt.% TiB2 composites when aged at 100 and 200°C during peak aged conditions. TEM images confirmed the presence of primary strengthening solute-rich phases after age hardening treatment such as θ’-Al2Cu and θ”-Al3Cu, which are responsible for hardness increment. An artificial neural network (ANN) model was created to predict the hardness trend of these composite samples using MATLAB R2021b, and results proved that the ANN model developed can be utilized as an effective tool to predict the hardness of treated composite samples. |
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Artificial Neural Network for Predicting Hardness of Multistage Solutionized and Artificially Aged LM4 + TiB2 CompositesSingle-stage solution heat treatment (SSHT)Multistage solution heat treatment (MSHT)Aging treatmentArtificial neural network (ANN)HardnessLM4 - Aluminium casting alloy (EN 1706 AC-45200)Abstract Aluminium casting alloy LM4 (EN 1706 AC-45200) composites with TiB2 (1, 2, and 3 wt.%) as reinforcements were produced using the two-stage stir casting method. OM and SEM study shows uniform and homogeneous reinforcement distribution in LM4 + TiB2 composites. As-cast composites were subjected to single-stage solution treatment at 520°C for 2 h and multistage solution treatment at 495 and 520°C for 2 and 4 h, followed by hot water quenching at 60°C and aging at 100 and 200°C for different time intervals. The hardness of as-cast and artificially aged composites were compared in both conditions. Compared to as-cast LM4 alloy, 20-45% improvement in hardness was observed for LM4 + TiB2 as-cast composites. 60-150% improvement in hardness was observed in artificially aged LM4 + 3 wt.% TiB2 composites when aged at 100 and 200°C during peak aged conditions. TEM images confirmed the presence of primary strengthening solute-rich phases after age hardening treatment such as θ’-Al2Cu and θ”-Al3Cu, which are responsible for hardness increment. An artificial neural network (ANN) model was created to predict the hardness trend of these composite samples using MATLAB R2021b, and results proved that the ANN model developed can be utilized as an effective tool to predict the hardness of treated composite samples.ABM, ABC, ABPol2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-14392022000100259Materials Research v.25 2022reponame:Materials research (São Carlos. Online)instname:Universidade Federal de São Carlos (UFSCAR)instacron:ABM ABC ABPOL10.1590/1980-5373-mr-2021-0557info:eu-repo/semantics/openAccessSrinivas,D.Shankar,GowriSharma,SathyashankaraShettar,ManjunathHiremath,Pavaneng2022-02-02T00:00:00Zoai:scielo:S1516-14392022000100259Revistahttp://www.scielo.br/mrPUBhttps://old.scielo.br/oai/scielo-oai.phpdedz@power.ufscar.br1980-53731516-1439opendoar:2022-02-02T00:00Materials research (São Carlos. Online) - Universidade Federal de São Carlos (UFSCAR)false |
dc.title.none.fl_str_mv |
Artificial Neural Network for Predicting Hardness of Multistage Solutionized and Artificially Aged LM4 + TiB2 Composites |
title |
Artificial Neural Network for Predicting Hardness of Multistage Solutionized and Artificially Aged LM4 + TiB2 Composites |
spellingShingle |
Artificial Neural Network for Predicting Hardness of Multistage Solutionized and Artificially Aged LM4 + TiB2 Composites Srinivas,D. Single-stage solution heat treatment (SSHT) Multistage solution heat treatment (MSHT) Aging treatment Artificial neural network (ANN) Hardness LM4 - Aluminium casting alloy (EN 1706 AC-45200) |
title_short |
Artificial Neural Network for Predicting Hardness of Multistage Solutionized and Artificially Aged LM4 + TiB2 Composites |
title_full |
Artificial Neural Network for Predicting Hardness of Multistage Solutionized and Artificially Aged LM4 + TiB2 Composites |
title_fullStr |
Artificial Neural Network for Predicting Hardness of Multistage Solutionized and Artificially Aged LM4 + TiB2 Composites |
title_full_unstemmed |
Artificial Neural Network for Predicting Hardness of Multistage Solutionized and Artificially Aged LM4 + TiB2 Composites |
title_sort |
Artificial Neural Network for Predicting Hardness of Multistage Solutionized and Artificially Aged LM4 + TiB2 Composites |
author |
Srinivas,D. |
author_facet |
Srinivas,D. Shankar,Gowri Sharma,Sathyashankara Shettar,Manjunath Hiremath,Pavan |
author_role |
author |
author2 |
Shankar,Gowri Sharma,Sathyashankara Shettar,Manjunath Hiremath,Pavan |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Srinivas,D. Shankar,Gowri Sharma,Sathyashankara Shettar,Manjunath Hiremath,Pavan |
dc.subject.por.fl_str_mv |
Single-stage solution heat treatment (SSHT) Multistage solution heat treatment (MSHT) Aging treatment Artificial neural network (ANN) Hardness LM4 - Aluminium casting alloy (EN 1706 AC-45200) |
topic |
Single-stage solution heat treatment (SSHT) Multistage solution heat treatment (MSHT) Aging treatment Artificial neural network (ANN) Hardness LM4 - Aluminium casting alloy (EN 1706 AC-45200) |
description |
Abstract Aluminium casting alloy LM4 (EN 1706 AC-45200) composites with TiB2 (1, 2, and 3 wt.%) as reinforcements were produced using the two-stage stir casting method. OM and SEM study shows uniform and homogeneous reinforcement distribution in LM4 + TiB2 composites. As-cast composites were subjected to single-stage solution treatment at 520°C for 2 h and multistage solution treatment at 495 and 520°C for 2 and 4 h, followed by hot water quenching at 60°C and aging at 100 and 200°C for different time intervals. The hardness of as-cast and artificially aged composites were compared in both conditions. Compared to as-cast LM4 alloy, 20-45% improvement in hardness was observed for LM4 + TiB2 as-cast composites. 60-150% improvement in hardness was observed in artificially aged LM4 + 3 wt.% TiB2 composites when aged at 100 and 200°C during peak aged conditions. TEM images confirmed the presence of primary strengthening solute-rich phases after age hardening treatment such as θ’-Al2Cu and θ”-Al3Cu, which are responsible for hardness increment. An artificial neural network (ANN) model was created to predict the hardness trend of these composite samples using MATLAB R2021b, and results proved that the ANN model developed can be utilized as an effective tool to predict the hardness of treated composite samples. |
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=S1516-14392022000100259 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-14392022000100259 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1980-5373-mr-2021-0557 |
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 |
_version_ |
1754212680622669824 |