MQL Strategies Applied in Ti-6Al-4V Alloy Milling-Comparative Analysis between Experimental Design and Artificial Neural Networks
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da FEI |
Texto Completo: | https://repositorio.fei.edu.br/handle/FEI/3499 |
Resumo: | This paper presents a study of the Ti-6Al-4V alloy milling under different lubrication conditions, using the minimum quantity lubrication approach. The chosen material is widely used in the industry due to its properties, although they present difficulties in terms of their machinability. A minimum quantity lubrication (MQL) prototype valve was built for this purpose, and machining followed a previously defined experimental design with three lubrication strategies. Speed, feed rate, and the depth of cut were considered as independent variables. As design-dependent variables, cutting forces, torque, and roughness were considered. The desirability optimization function was used in order to obtain the best input data indications, in order to minimize cutting and roughness efforts. Supervised artificial neural networks of the multilayer perceptron type were created and tested, and their responses were compared statistically to the results of the factorial design. It was noted that the variables that most influenced the machining-dependent variables were the feed rate and the depth of cut. A lower roughness value was achieved with MQL only with the use of cutting fluid with graphite. Statistical analysis demonstrated that artificial neural network and the experimental design predict similar results. |
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Creative Commons "Este é um artigo publicado em acesso aberto sob uma licença Creative Commons (CC BY 4.0). Fonte: https://www.mdpi.com/1996-1944/13/17/3828. Acesso em: 03 jan. 2022.info:eu-repo/semantics/openAccessPASCHOALINOTO, NELSON WILSONBATALHA, GILMAR FERREIRAEd Claudio BordinassiFERRER, JORGE ANTONIO GILESLIMA FILHO, ADERVAL FERREIRA DERIBEIRO, GLEICY DE L. X.CARDOSO, CRISTIANO2022-01-03T19:52:59Z2022-01-03T19:52:59Z2020-08-30PASCHOALINOTO, N. W.; BATALHA, G. F.; BORDINASSI E. C. ; FERRER, J. A. G.; LIMA FILHO, A. F. DE; RIBEIRO, G. DE L. X.; CARDOSO, C. MQL strategies applied in Ti-6Al-4V Alloy Milling-Comparative Analysis between experimental design and artificial neural networks. Materials, v. 13, n. 17, p. 3828-3857, 2020.1996-1944https://repositorio.fei.edu.br/handle/FEI/349910.3390/ma13173828This paper presents a study of the Ti-6Al-4V alloy milling under different lubrication conditions, using the minimum quantity lubrication approach. The chosen material is widely used in the industry due to its properties, although they present difficulties in terms of their machinability. A minimum quantity lubrication (MQL) prototype valve was built for this purpose, and machining followed a previously defined experimental design with three lubrication strategies. Speed, feed rate, and the depth of cut were considered as independent variables. As design-dependent variables, cutting forces, torque, and roughness were considered. The desirability optimization function was used in order to obtain the best input data indications, in order to minimize cutting and roughness efforts. Supervised artificial neural networks of the multilayer perceptron type were created and tested, and their responses were compared statistically to the results of the factorial design. It was noted that the variables that most influenced the machining-dependent variables were the feed rate and the depth of cut. A lower roughness value was achieved with MQL only with the use of cutting fluid with graphite. Statistical analysis demonstrated that artificial neural network and the experimental design predict similar results.131738283857MaterialsTi-6AL-4VMQLMachiningMillingLubricationOptimizationMQL Strategies Applied in Ti-6Al-4V Alloy Milling-Comparative Analysis between Experimental Design and Artificial Neural Networksinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlereponame:Biblioteca Digital de Teses e Dissertações da FEIinstname:Centro Universitário da Fundação Educacional Inaciana (FEI)instacron:FEIhttps://www.mdpi.com/1996-1944/13/17/3828ORIGINALBordinassi_pdfBordinassi_pdfapplication/pdf22718859https://repositorio.fei.edu.br/bitstream/FEI/3499/1/Bordinassi_pdf10ae128353870a9d2e457b838fff6890MD51TEXTBordinassi_pdf.txtBordinassi_pdf.txtExtracted texttext/plain86424https://repositorio.fei.edu.br/bitstream/FEI/3499/2/Bordinassi_pdf.txt52370ae207c059482317dc19e70ad5dcMD52THUMBNAILBordinassi_pdf.jpgBordinassi_pdf.jpgGenerated Thumbnailimage/jpeg1615https://repositorio.fei.edu.br/bitstream/FEI/3499/3/Bordinassi_pdf.jpg84008b2a51b2bf67e928af0c09a3a91cMD53FEI/34992022-01-04 04:00:31.839Biblioteca Digital de Teses e Dissertaçõeshttp://sofia.fei.edu.br/pergamum/biblioteca/PRI |
dc.title.pt_BR.fl_str_mv |
MQL Strategies Applied in Ti-6Al-4V Alloy Milling-Comparative Analysis between Experimental Design and Artificial Neural Networks |
title |
MQL Strategies Applied in Ti-6Al-4V Alloy Milling-Comparative Analysis between Experimental Design and Artificial Neural Networks |
spellingShingle |
MQL Strategies Applied in Ti-6Al-4V Alloy Milling-Comparative Analysis between Experimental Design and Artificial Neural Networks PASCHOALINOTO, NELSON WILSON Ti-6AL-4V MQL Machining Milling Lubrication Optimization |
title_short |
MQL Strategies Applied in Ti-6Al-4V Alloy Milling-Comparative Analysis between Experimental Design and Artificial Neural Networks |
title_full |
MQL Strategies Applied in Ti-6Al-4V Alloy Milling-Comparative Analysis between Experimental Design and Artificial Neural Networks |
title_fullStr |
MQL Strategies Applied in Ti-6Al-4V Alloy Milling-Comparative Analysis between Experimental Design and Artificial Neural Networks |
title_full_unstemmed |
MQL Strategies Applied in Ti-6Al-4V Alloy Milling-Comparative Analysis between Experimental Design and Artificial Neural Networks |
title_sort |
MQL Strategies Applied in Ti-6Al-4V Alloy Milling-Comparative Analysis between Experimental Design and Artificial Neural Networks |
author |
PASCHOALINOTO, NELSON WILSON |
author_facet |
PASCHOALINOTO, NELSON WILSON BATALHA, GILMAR FERREIRA Ed Claudio Bordinassi FERRER, JORGE ANTONIO GILES LIMA FILHO, ADERVAL FERREIRA DE RIBEIRO, GLEICY DE L. X. CARDOSO, CRISTIANO |
author_role |
author |
author2 |
BATALHA, GILMAR FERREIRA Ed Claudio Bordinassi FERRER, JORGE ANTONIO GILES LIMA FILHO, ADERVAL FERREIRA DE RIBEIRO, GLEICY DE L. X. CARDOSO, CRISTIANO |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
PASCHOALINOTO, NELSON WILSON BATALHA, GILMAR FERREIRA Ed Claudio Bordinassi FERRER, JORGE ANTONIO GILES LIMA FILHO, ADERVAL FERREIRA DE RIBEIRO, GLEICY DE L. X. CARDOSO, CRISTIANO |
dc.subject.por.fl_str_mv |
Ti-6AL-4V MQL Machining Milling Lubrication Optimization |
topic |
Ti-6AL-4V MQL Machining Milling Lubrication Optimization |
description |
This paper presents a study of the Ti-6Al-4V alloy milling under different lubrication conditions, using the minimum quantity lubrication approach. The chosen material is widely used in the industry due to its properties, although they present difficulties in terms of their machinability. A minimum quantity lubrication (MQL) prototype valve was built for this purpose, and machining followed a previously defined experimental design with three lubrication strategies. Speed, feed rate, and the depth of cut were considered as independent variables. As design-dependent variables, cutting forces, torque, and roughness were considered. The desirability optimization function was used in order to obtain the best input data indications, in order to minimize cutting and roughness efforts. Supervised artificial neural networks of the multilayer perceptron type were created and tested, and their responses were compared statistically to the results of the factorial design. It was noted that the variables that most influenced the machining-dependent variables were the feed rate and the depth of cut. A lower roughness value was achieved with MQL only with the use of cutting fluid with graphite. Statistical analysis demonstrated that artificial neural network and the experimental design predict similar results. |
publishDate |
2020 |
dc.date.issued.fl_str_mv |
2020-08-30 |
dc.date.accessioned.fl_str_mv |
2022-01-03T19:52:59Z |
dc.date.available.fl_str_mv |
2022-01-03T19:52:59Z |
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.citation.fl_str_mv |
PASCHOALINOTO, N. W.; BATALHA, G. F.; BORDINASSI E. C. ; FERRER, J. A. G.; LIMA FILHO, A. F. DE; RIBEIRO, G. DE L. X.; CARDOSO, C. MQL strategies applied in Ti-6Al-4V Alloy Milling-Comparative Analysis between experimental design and artificial neural networks. Materials, v. 13, n. 17, p. 3828-3857, 2020. |
dc.identifier.uri.fl_str_mv |
https://repositorio.fei.edu.br/handle/FEI/3499 |
dc.identifier.issn.none.fl_str_mv |
1996-1944 |
dc.identifier.doi.none.fl_str_mv |
10.3390/ma13173828 |
identifier_str_mv |
PASCHOALINOTO, N. W.; BATALHA, G. F.; BORDINASSI E. C. ; FERRER, J. A. G.; LIMA FILHO, A. F. DE; RIBEIRO, G. DE L. X.; CARDOSO, C. MQL strategies applied in Ti-6Al-4V Alloy Milling-Comparative Analysis between experimental design and artificial neural networks. Materials, v. 13, n. 17, p. 3828-3857, 2020. 1996-1944 10.3390/ma13173828 |
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https://repositorio.fei.edu.br/handle/FEI/3499 |
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Materials |
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openAccess |
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