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

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          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.

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          State-of-the-art in artificial neural network applications: A survey

          This is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of current and emerging trends in ANN applications research and area of focus for researchers. Additionally, the study presents ANN application challenges, contributions, compare performances and critiques methods. The study covers many applications of ANN techniques in various disciplines which include computing, science, engineering, medicine, environmental, agriculture, mining, technology, climate, business, arts, and nanotechnology, etc. The study assesses ANN contributions, compare performances and critiques methods. The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems. Therefore, we proposed feedforward and feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance. Moreover, we recommend that instead of applying a single method, future research can focus on combining ANN models into one network-wide application.
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            Hybrid cryogenic MQL for improving tool life in machining of Ti-6Al-4V titanium alloy

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              Design of experiments and focused grid search for neural network parameter optimization

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                Author and article information

                Journal
                Materials (Basel)
                Materials (Basel)
                materials
                Materials
                MDPI
                1996-1944
                30 August 2020
                September 2020
                : 13
                : 17
                : 3828
                Affiliations
                [1 ]Faculty of Mechatronic Technology, National Service for Industrial Training (SENAI-SP), São Caetano do Sul, SP 09572-300, Brazil; jorge.ferrer@ 123456sp.senai.br (J.A.G.F.); aderval.filho@ 123456sp.senai.br (A.F.d.L.F.)
                [2 ]Department of Mechatronics and Mechanical Systems Engineering, Polytechnic School of Engineering of the University of Sao Paulo (USP), São Paulo, SP 05508-900, Brazil; gilmar.batalha@ 123456usp.br
                [3 ]Department of Mechanical Engineering, University Center of the Mauá Institute of Technology (IMT), São Caetano do Sul, SP 09580-900, Brazil
                [4 ]Technology Faculty of Mauá, The Paula Souza State Center for Technological Education (CEETEPS), São Paulo, SP 09390-120, Brazil
                [5 ]Department of Mechanical Engineering, University Center of Educational Foundation of Ignatius (FEI), São Bernardo do Campo, SP 09850-901, Brazil
                [6 ]Department of Mechanical Engineering, University of Taubaté (UNITAU), Taubaté, SP 12020-270, Brazil
                [7 ]Institute for Innovation in Advanced Manufacturing and Microfabrication, National Service for Industrial Training (SENAI-SP), São Paulo, SP 04757-000, Brazil; gleicy.ribeiro@ 123456sp.senai.br (G.d.L.X.R.); cristiano.cardoso@ 123456sp.senai.br (C.C.)
                Author notes
                [* ]Correspondence: nelson.paschoalinoto@ 123456maua.br (N.W.P.); ecb@ 123456maua.br or edbordinassi@ 123456fei.edu.br (E.C.B.); Tel.: +55-011-4239-3000 (N.W.P.)
                Author information
                https://orcid.org/0000-0003-2094-6031
                Article
                materials-13-03828
                10.3390/ma13173828
                7504553
                32872596
                0e8c3750-dfb5-4580-94a5-1da420393868
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 02 August 2020
                : 26 August 2020
                Categories
                Article

                ti-6al-4v,mql,machining,milling,lubrication,optimization
                ti-6al-4v, mql, machining, milling, lubrication, optimization

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