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

Materials

EdClaudio Bordinassi¹  GilmarFerreira Batalha²  NelsonWilson Paschoalinoto³  AdervalFerreira de Lima Filho³  JorgeAntonio Giles Ferrer³  Gleicyde L. X. Ribeiro⁴  Cristiano Cardoso⁴ 

[1] Department of Mechanical Engineering, University Centre of the Mauá Institute of Technology (IMT), São Caetano do Sul, SP 09580-900, Brazil;Department of Mechatronics and Mechanical Systems Engineering, Polytechnic School of Engineering of the University of Sao Paulo (USP), São Paulo, SP 05508-900, Brazil;Faculty of Mechatronic Technology, National Service for Industrial Training (SENAI-SP), São Caetano do Sul, SP 09572-300, Brazil;Institute for Innovation in Advanced Manufacturing and Microfabrication, National Service for Industrial Training (SENAI-SP), São Paulo, SP 04757-000, Brazil;

关键词: Ti-6AL-4V;    MQL;    machining;    milling;    lubrication;    optimization;   

DOI  :  10.3390/ma13173828

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown