Experimental Investigations on µED Milling of Inconel 718 with Nano SiC Abrasive Mixed Dielectric
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=S1516-14392022000100217 |
Resumo: | Abstract Micro electro-discharge machining is one of the efficient processes to create three-dimensional micro features of metallic components for various applications. Powder mixed EDM improves the machining rate and reduces the surface roughness by evenly distributing the spark. The present studydemonstrates the effect of SiC nanopowder on the machining of Inconel 718 at different Discharge Energy Regimes (DER). Significant improvement in MRR, reduction in TWR and surface roughness were observed in nanopowder mixed micro-EDM (NPMμEDM) compared with micro-EDM. The nano additive considerably improved the Material Removal Rate (MRR) by163% and reduced the Tool Wear Rate (TWR)and surface roughness by 24%, 17% respectively. Models were created to predict the Surface Roughness in NPMμEDM using two different approaches namely Support Vector Regression (SVR) and Random Forest Machine (RFM). Both SVR and RFM models were able to predict the Ra value with better accuracies. |
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Materials research (São Carlos. Online) |
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|
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Experimental Investigations on µED Milling of Inconel 718 with Nano SiC Abrasive Mixed DielectricMicro-EDMSilicon carbide nanoparticleInconel 718Machine learningAbstract Micro electro-discharge machining is one of the efficient processes to create three-dimensional micro features of metallic components for various applications. Powder mixed EDM improves the machining rate and reduces the surface roughness by evenly distributing the spark. The present studydemonstrates the effect of SiC nanopowder on the machining of Inconel 718 at different Discharge Energy Regimes (DER). Significant improvement in MRR, reduction in TWR and surface roughness were observed in nanopowder mixed micro-EDM (NPMμEDM) compared with micro-EDM. The nano additive considerably improved the Material Removal Rate (MRR) by163% and reduced the Tool Wear Rate (TWR)and surface roughness by 24%, 17% respectively. Models were created to predict the Surface Roughness in NPMμEDM using two different approaches namely Support Vector Regression (SVR) and Random Forest Machine (RFM). Both SVR and RFM models were able to predict the Ra value with better accuracies.ABM, ABC, ABPol2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-14392022000100217Materials Research v.25 2022reponame:Materials research (São Carlos. Online)instname:Universidade Federal de São Carlos (UFSCAR)instacron:ABM ABC ABPOL10.1590/1980-5373-mr-2021-0468info:eu-repo/semantics/openAccessElumalai,B.Gowri,S.Hariharan,P.Pillai,K.V. Aruneng2021-11-19T00:00:00Zoai:scielo:S1516-14392022000100217Revistahttp://www.scielo.br/mrPUBhttps://old.scielo.br/oai/scielo-oai.phpdedz@power.ufscar.br1980-53731516-1439opendoar:2021-11-19T00:00Materials research (São Carlos. Online) - Universidade Federal de São Carlos (UFSCAR)false |
dc.title.none.fl_str_mv |
Experimental Investigations on µED Milling of Inconel 718 with Nano SiC Abrasive Mixed Dielectric |
title |
Experimental Investigations on µED Milling of Inconel 718 with Nano SiC Abrasive Mixed Dielectric |
spellingShingle |
Experimental Investigations on µED Milling of Inconel 718 with Nano SiC Abrasive Mixed Dielectric Elumalai,B. Micro-EDM Silicon carbide nanoparticle Inconel 718 Machine learning |
title_short |
Experimental Investigations on µED Milling of Inconel 718 with Nano SiC Abrasive Mixed Dielectric |
title_full |
Experimental Investigations on µED Milling of Inconel 718 with Nano SiC Abrasive Mixed Dielectric |
title_fullStr |
Experimental Investigations on µED Milling of Inconel 718 with Nano SiC Abrasive Mixed Dielectric |
title_full_unstemmed |
Experimental Investigations on µED Milling of Inconel 718 with Nano SiC Abrasive Mixed Dielectric |
title_sort |
Experimental Investigations on µED Milling of Inconel 718 with Nano SiC Abrasive Mixed Dielectric |
author |
Elumalai,B. |
author_facet |
Elumalai,B. Gowri,S. Hariharan,P. Pillai,K.V. Arun |
author_role |
author |
author2 |
Gowri,S. Hariharan,P. Pillai,K.V. Arun |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Elumalai,B. Gowri,S. Hariharan,P. Pillai,K.V. Arun |
dc.subject.por.fl_str_mv |
Micro-EDM Silicon carbide nanoparticle Inconel 718 Machine learning |
topic |
Micro-EDM Silicon carbide nanoparticle Inconel 718 Machine learning |
description |
Abstract Micro electro-discharge machining is one of the efficient processes to create three-dimensional micro features of metallic components for various applications. Powder mixed EDM improves the machining rate and reduces the surface roughness by evenly distributing the spark. The present studydemonstrates the effect of SiC nanopowder on the machining of Inconel 718 at different Discharge Energy Regimes (DER). Significant improvement in MRR, reduction in TWR and surface roughness were observed in nanopowder mixed micro-EDM (NPMμEDM) compared with micro-EDM. The nano additive considerably improved the Material Removal Rate (MRR) by163% and reduced the Tool Wear Rate (TWR)and surface roughness by 24%, 17% respectively. Models were created to predict the Surface Roughness in NPMμEDM using two different approaches namely Support Vector Regression (SVR) and Random Forest Machine (RFM). Both SVR and RFM models were able to predict the Ra value with better accuracies. |
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=S1516-14392022000100217 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-14392022000100217 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1980-5373-mr-2021-0468 |
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_ |
1754212680009252864 |