Neural network modeling of cutting fluid impact on energy consumption during turning

Abstract

© 2016 Published by Faculty of Engineering. This paper presents a part of research on power consumption differences between various cutting fluids used during turning operations. An attempt was made to study the possibility of artificial neural network to model the behavior function and predicting the electrical power consumption. Friction factor of examined cutting fluids was also measured to describe a more complete picture of investigated cutting fluids characteristics. It was discovered that wide spectrum of characteristics is present in today’s market and that artificial neural networks are suitable for purpose of modeling the power consumption of the lathe during machining. This paper could be used as a foundation for later database building where it would be possible to predict how certain cutting fluid will behave in a specific machining parameter combination.