Title: Study on the influence of MQL and cutting conditions on machinability of brass using Artificial Neural Network

Authors: V.N. Gaitonde, S.R. Karnik, J. Paulo Davim

Addresses: Department of Industrial and Production Engineering, B.V.B. College of Engineering and Technology, Hubli 580 031, Karnataka, India. ' Department of Electrical and Electronics Engineering, B.V.B. College of Engineering and Technology, Hubli 580 031, Karnataka, India. ' Department of Mechanical Engineering, University of Aveiro, Campus Santiago, Aveiro 3810-193, Portugal

Abstract: The Minimum Quantity of Lubrication (MQL) in machining is an alternative to dry or flood lubricating system. In the current study, an Artificial Neural Network (ANN) has been employed to analyse the effects of cutting speed, feed rate and MQL on specific cutting force and surface roughness in turning of brass. The research suggests a combination of MQL in medium to high range, higher feed rate with cutting speed in medium range is necessary to minimise specific cutting force. On the other hand, lower values of feed rate and cutting speed with high MQL is essential for minimising surface roughness.

Keywords: turning; brass; carbide tooling; MQL; minimum quantity lubrication; specific cutting force; surface roughness; ANN; artificial neural networks; cutting speed; feed rate.

DOI: 10.1504/IJMPT.2010.029466

International Journal of Materials and Product Technology, 2010 Vol.37 No.1/2, pp.155 - 172

Published online: 30 Nov 2009 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article