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Design of fuzzy expert system for predicting of surface roughness in high-pressure jet assisted turning using bioinspired algorithms

Published online by Cambridge University Press:  29 April 2015

Davorin Kramar ,
Djordje Cica ,
Branislav Sredanovic  and
Janez Kopac
Davorin Kramar*
Affiliation:
Faculty of Mechanical Engineering, University of Ljubljana, Askerceva, Ljubljana, Slovenia
Djordje Cica
Affiliation:
Faculty of Mechanical Engineering, University of Banja Luka, Bulevar Vojvode Stepe Stepanovica, Banja Luka, Bosnia and Herzegovina
Branislav Sredanovic
Affiliation:
Faculty of Mechanical Engineering, University of Banja Luka, Bulevar Vojvode Stepe Stepanovica, Banja Luka, Bosnia and Herzegovina
Janez Kopac
Affiliation:
Faculty of Mechanical Engineering, University of Ljubljana, Askerceva, Ljubljana, Slovenia
*
Reprint requests to: Davorin Kramar, University of Ljubljana, Faculty of Mechanical Engineering, Askerceva 6, 1000 Ljubljana, Slovenia. E-mail: davorin.kramar@fs.uni-lj.si
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Abstract

The surface roughness of the machined parts is one of the most important factors that have considerable influence on the quality and functional properties of products. The objective of this study is development of a surface roughness prediction model for machining Inconel 718 in high-pressure jet assisted turning using the fuzzy expert system, where the fuzzy system is optimized using two bioinspired algorithms: genetic algorithm and particle swarm optimization. The effect of various influential machining parameters, such as diameter of the nozzle, pressure of the jet, cutting speed, feed rate, and distance between the impact point of the jet and cutting edge were taken into consideration in this study. The predicted surface roughness values obtained from developed fuzzy expert systems were compared with the experimental data, and the results indicate that proposed systems can be effectively used to estimate the surface roughness in high-pressure jet assisted turning.

Keywords

Fuzzy LogicGenetic AlgorithmHigh-Pressure Jet Assisted TurningParticle Swarm OptimizationRoughness
Type
Regular Articles
Information
AI EDAM , Volume 30 , Issue 1 , February 2016 , pp. 96 - 106
Copyright
Copyright © Cambridge University Press 2015 

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