Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-24T16:58:43.167Z Has data issue: false hasContentIssue false
  • English
  • Français

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: [email protected]
Get access Rights & Permissions [Opens in a new window]

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Abburi, N.R., & Dixit, U.S. (2006). A knowledge based system for the prediction of surface roughness in turning process. Robotics and Computer Integrated Manufacturing 22(4), 363–372.CrossRefGoogle Scholar
Chandrasekaran, M., & Devarasiddappa, D. (2014). Artificial neural network modeling for surface roughness prediction in cylindrical grinding of Al-SiCp metal matrix composites and ANOVA analysis. Advances in Production Engineering & Management 9(2), 59–70.CrossRefGoogle Scholar
Chandrasekaran, M., Muralidhar, M., Krishna, C.M., & Dixit, U.S. (2010). Application of soft computing techniques in machining performance prediction and optimization: a literature review. International Journal of Advanced Manufacturing Technology 46(5), 445–464.CrossRefGoogle Scholar
Courbon, C., Kramar, D., Krajnik, P., Pusavec, F., Rech, J., & Kopac, J. (2009). Investigation of machining performance in high-pressure jet assisted turning of Inconel 718: an experimental study. International Journal of Machine Tools & Manufacture 49(14), 1114–1125.CrossRefGoogle Scholar
Courbon, C., Sajn, V., Kramar, D., Recha, J., Kosel, F., & Kopac, J. (2011). Investigation of machining performance in high pressure jet assisted turning of Inconel 718: a numerical model. Journal of Materials Processing Technology 211(11), 1834–1851.CrossRefGoogle Scholar
Ezugwu, E.O., & Bonney, J. (2004). Effect of high-pressure coolant supply when machining nickel-base Inconel 718 alloy with coated carbide tools. Journal of Materials Processing Technology 153–154, 1045–1050.CrossRefGoogle Scholar
Fei, J., & Jawahir, I.S. (1992). A fuzzy knowledge-based system for predicting surface roughness in finish turning. Proc. IEEE Int. Conf. Fuzzy Systems, pp. 899–906, San Diego, CA, March 8–12.CrossRefGoogle Scholar
Hrelja, M., Klancnik, S., Balic, J., & Brezocnik, M. (2014 a). Modelling of a turning process using the gravitational search algorithm. International Journal of Simulation Modelling 13(1), 30–41.CrossRefGoogle Scholar
Hrelja, M., Klancnik, S., Irgolic, T., Paulic, M., Jurkovic, Z., Balic, J., & Brezocnik, M. (2014 b). Particle swarm optimization approach for modelling a turning process. Advances in Production Engineering & Management 9(1), 21–30.CrossRefGoogle Scholar
Jiao, Y., Lei, S., Pei, Z.J., & Lee, E.S. (2004). Fuzzy adaptive networks in machining process modeling: surface roughness prediction for turning operations. International Journal of Machine Tools & Manufacture 44(15), 1643–1651.CrossRefGoogle Scholar
Kramar, D., & Kopac, J. (2009). High pressure cooling in the machining of hard-to-machine materials. Journal of Mechanical Engineering 55(11), 685–694.Google Scholar
Mohd Hadzley, A.B., Izamshah, R., Siti Sarah, A., & Nurul Fatin, M. (2013). Finite element model of machining with high pressure coolant for Ti-6Al-4V alloy. Procedia Engineering 53, 624–631.CrossRefGoogle Scholar
Nandi, A.K. (2006). TSK-type FLC using a combined LR and GA: surface roughness prediction in ultraprecision turning. Journal of Materials Processing Technology 178(1–3), 200–210.CrossRefGoogle Scholar
Nandi, A.K., & Pratihar, D.K. (2004). An expert system based on FBFN using a GA to predict surface finish in ultra-precision turning. Journal of Materials Processing Technology 155–156, 1150–1156.CrossRefGoogle Scholar
Rajasekaran, T., Palanikumar, K., & Vinayagam, B.K. (2011). Application of fuzzy logic for modeling surface roughness in turning CFRP composites using CBN tool. Production Engineering 5(2), 191–199.CrossRefGoogle Scholar
Roy, S.S. (2006). Design of genetic-fuzzy expert system for predicting surface finish in ultra-precision diamond turning of metal matrix composite. Journal of Materials Processing Technology 173(3), 337–344.CrossRefGoogle Scholar
Roy, S.S. (2007). An application of the adaptive neuro-fuzzy inference system for prediction of surface roughness in turning. International Journal of Computer Applications in Technology 28, 281–288.CrossRefGoogle Scholar
Sekulic, M., Kopac, J., Gostimirovic, M., & Kramar, D. (2013). Optimization of high-pressure jet assisted turning process by Taguchi method. Advances in Production Engineering & Management 8(1), 5–12.CrossRefGoogle Scholar
Trdan, U., & Grum, J. (2014). SEM/EDS characterization of laser shock peening effect on localized corrosion of Al alloy in a near natural chloride environment. Corrosion Science 82, 328–338.CrossRefGoogle Scholar
Yünlü, L., Çolak, O., & Kurbanoğlu, C. (2014). Taguchi DOE analysis of surface integrity for high pressure jet assisted machining of Inconel 718. Procedia CIRP 13, 333–338.CrossRefGoogle Scholar