Please use this identifier to cite or link to this item: https://repositorio.fei.edu.br/handle/FEI/3499
Title: MQL Strategies Applied in Ti-6Al-4V Alloy Milling-Comparative Analysis between Experimental Design and Artificial Neural Networks
Authors: 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
Issue Date: 30-Aug-2020
Abstract: 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.
Keywords: Ti-6AL-4V
MQL
Machining
Milling
Lubrication
Optimization
Journal: Materials
Citation: 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.
Access Type: Acesso Aberto
DOI: 10.3390/ma13173828
Appears in Collections:Artigos

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