A comparative study of ANN and FES for predicting of cutting forces and tool tip temperature in turning

Authors:
Ilker Ali Ozkan;Ismail Saritas;Suleyman Yaldiz
Affiliations:
Selcuk University;Selcuk University;Selcuk University
Venue:
Proceedings of the 11th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing on International Conference on Computer Systems and Technologies
Year:
2010

Citing 5
Cited 0

Force parameters for on-line tool wear estimation: a neural network approach

Neural Networks
A course in fuzzy systems and control

A course in fuzzy systems and control
Neural network design

Neural network design
Bit-Slice Microprocessor Design

Bit-Slice Microprocessor Design
The experimental investigation of the effects of uncoated, PVD- and CVD-coated cemented carbide inserts and cutting parameters on surface roughness in CNC turning and its prediction using artificial neural networks

Robotics and Computer-Integrated Manufacturing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this study, fuzzy expert system (FES) and artificial neural network (ANN) models are designed for the estimation of cutting forces in turning operations. On designed models, cutting forces and experimental temperature data obtained from different cutting conditions were used in process of turning. Cutting forces at different cutting conditions and temperature values can be estimated with the help of developed models. The results obtained with these models, compared with the experimental data. The regression values were found as 0.99505 between the Experiment-FES and, 0.9888 between Experiment-ANN in the analysis. As a result, the both artificial intelligence (AI) methods have made successful modeling, but it's seen that, realized FES model has more successful results than the ANN model in the process of estimation of cutting forces.