Page 163 - Data Science Algorithms in a Week

In: Artificial Intelligence ISBN: 978-1-53612-677-8
Editors: L. Rabelo, S. Bhide and E. Gutierrez © 2018 Nova Science Publishers, Inc.

Chapter 7

THE ESTIMATION OF CUTTING FORCES IN THE

TURNING OF INCONEL 718 ASSISTED WITH A HIGH

PRESSURE COOLANT USING BIO-INSPIRED

ARTIFICIAL NEURAL NETWORKS

1,*
Djordje Cica and Davorin Kramar
2
1 Univeristy of Banja Luka, Faculty of Mechanical Engineering, Stepe Stepanovica
71, Banja Luka, Bosnia and Herzegovina
2 Univeristy of Ljubljana, Faculty of Mechanical Engineering, Askerceva 6,
Ljubljana, Slovenia

ABSTRACT

Accurate prediction of cutting forces is very essential due to their significant impacts
on product quality. During the past two decades, high pressure cooling (HPC) technique
is starting to be established as a method for substantial increase of productivity in the
metal cutting industry. This technique has proven to be very effective in machining of
hard-to-machine materials such as the nickel-based alloy Inconel 718, which is
characterized by low efficiency of machining process. However, modeling of cutting
forces under HPC conditions is very difficult task due to complex relations between large
numbers of process parameters such are pressure of the jet, diameter of the nozzle,
cutting speed, feed, etc. One of the ways to overcome such difficulty is to implement
models based on the artificial intelligence tools like artificial neural network (ANN),
genetic algorithm (GA), particle swarm optimization (PSO), fuzzy logic, etc. as an
alternative to conventional approaches. Regarding the feedforward ANN training, the

* Corresponding Author Email: djordjecica@gmail.com.