MQL Strategies Applied in Ti-6Al-4V Alloy Milling-Comparative Analysis between Experimental Design and Artificial Neural Networks

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dc.contributor.authorPASCHOALINOTO, NELSON WILSON
dc.contributor.authorBATALHA, GILMAR FERREIRA
dc.contributor.authorEd Claudio Bordinassi
dc.contributor.authorFERRER, JORGE ANTONIO GILES
dc.contributor.authorLIMA FILHO, ADERVAL FERREIRA DE
dc.contributor.authorRIBEIRO, GLEICY DE L. X.
dc.contributor.authorCARDOSO, CRISTIANO
dc.date.accessioned2022-01-03T19:52:59Z
dc.date.available2022-01-03T19:52:59Z
dc.date.issued2020-08-30
dc.description.abstractThis 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.
dc.description.firstpage3828
dc.description.issuenumber17
dc.description.lastpage3857
dc.description.volume13
dc.identifier.citationPASCHOALINOTO, 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.doi10.3390/ma13173828
dc.identifier.issn1996-1944
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/3499
dc.relation.ispartofMaterials
dc.rightsAcesso Aberto
dc.rights.licenseCreative 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.
dc.subjectTi-6AL-4V
dc.subjectMQL
dc.subjectMachining
dc.subjectMilling
dc.subjectLubrication
dc.subjectOptimization
dc.titleMQL Strategies Applied in Ti-6Al-4V Alloy Milling-Comparative Analysis between Experimental Design and Artificial Neural Networkspt_BR
dc.typeArtigopt_BR
fei.scopus.citations6
fei.scopus.eid2-s2.0-85090932565
fei.scopus.updated2024-03-04
fei.source.urlhttps://www.mdpi.com/1996-1944/13/17/3828
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