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Licensed Unlicensed Requires Authentication Published by De Gruyter May 26, 2013

Use of Grey-Taguchi Method for the Optimization of Oblique Turning Process of AZ91D Magnesium Alloy

Verwendung der Grey-Taguchi Methode für die Optimierung von Drehprozessen der Magnesiumlegierung AZ91D
  • Bariş Buldum , Uğur Eşme , Mustafa Kemal Külekci , Aydin Şik and Yiğit Kazançoğlu
From the journal Materials Testing


This study investigated the multi-response optimization of turning process for an optimal parametric combination to yield minimum cutting forces and surface roughness with maximum material removal rate (MRR) using the combination of Grey relational analysis (GRA) and Taguchi method. Nine experimental runs based on an orthogonal array of Taguchi method were performed to derive objective functions to be optimized within experimental domain. The objective functions have been selected in relation to parameters of cutting process: cutting force, surface roughness and MRR. The Taguchi approach followed by Grey relational analysis to solve the multi-response optimization problem. The significance of factors on overall quality characteristics of the cutting process has also been evaluated quantitatively by the analysis of variance method (ANOVA). Optimal results have been verified through additional experiments. This shows proper selection of the cutting parameters produces, high material removal rate with better surface roughness and lower cutting force.


In der diesem Beitrag zugrunde liegenden Studie wurde eine Mehrfach-Antwort-Optimierung des Drehprozesses in Hinblick auf eine optimale Parameterkombination zur Erreichung minimaler Schnittkräfte und Oberflächenrauheiten bei maximaler Materialabtragsrate (MMR) durchgeführt, in dem die Grey Relational Analyse (GRA) mit der Taguchi Methode kombiniert wurde. Neun experimentelle Runs, die auf einem orthogonalen Feld der Taguchi-Methode basieren, wurden durchgeführt, um objektive Funktionen abzuleiten, die mittels der experimentellen Ergebnisse optimiert wurden. Die objektiven Funktionen wurden in Hinblick auf die Parameter des Schneidprozesses, nämlich der Schnittkraft, der Oberflächenrauheit und der MRR gewählt. Dem Taguchi-Ansatz folgte eine GRA um die Mehrfach-Antwort-Aufgabe zu lösen. Die Signifikanz der Faktoren auf die allgemeinen Qualitätsmerkmale des Schneidprozesses wurden ebenfalls quantitativ mittels der Varianzanalyse (ANOVA) ausgewertet. Die optimalen Ergebnisse wurden experimentell verifiziert. Eine richtige Auswahl der Schneidparameter zeigt eine hohe Materialabtragsrate mit besserer Oberflächenrauheit und niedrigerer Schnittgeschwindigkeit.

Bariş Buldum, born in 1979 in Mersin, completed his primary education in Mersin and bachelor from the Faculty of Technical Education, Gazi University, Turkey, in 2003 and master degree from Institute of Science, Gazi University, Turkey, in 2006. He's PhD student in Gazi University since 2009. His interests are machinability of metals, lightmetal cutting, and design as well as construction. He's lecturer at Mersin University, Turkey, since 1997. Now, he is working in advanced technology education, Research and Application Center in Mersin University, Turkey.

Ugur Esme is assistant professor Dr. in Mersin University Tarsus Technical Education Faculty, Turkey. He obtained his PhD degree from Cukurova University Department, Turkey, of mechanical engineering in 2006. His research areas include CAD/CAM technology, welding, modelling, designing, and water jet cutting applications as well as neural network.

Mustafa Kemal Kulekci is professor of Faculty of Tarsus Technical Education, Department of Machine Education, Mersin University, Turkey. He obtained his PhD degree from Gazi University, Turkey, in 2000. His research interests include CAD/CAM, friction stir welding, machinability of materials, and water-jet cutting applications.

Aydin Şik completed bachelor at Faculty of Industrial Technical Education, Gazi University, Turkey, master and Phd. degree at Institute of Science, Gazi University, Turkey. His interests are welding, machinability of metals, design, and construction. He is lecturer at Gazi University, Turkey, since 1995. Now, he is working in Faculty of Industrial Technical Education of Gazi University, Turkey. He is assistant professor in Gazi University since 2011.

Yigit Kazancoglu is assistant professor Dr. in Izmir University of Economics, Dept. of Business Administration, Turkey. He received his BS degree from Industrial Engineering Dept. of Eastern Mediterranean University, MBA degree from Coventry Univertsity and Izmir University of Economics, Turkey, and PhD degree in Ege University, Turkey, in operations management. His work at the university involves giving courses and conducting research in the areas of production planning, operations management and operations research. He is the author of a number of international publications on these subjects.


1 J. P.Fabricio, P. P.Anderson, P. B.Pedro, F. J.Roberto, B. S.Messias: Optimization of Radial Basis Function neural network employed for prediction of surface roughness in hard turning process using Taguchi—s orthogonal arrays, Expert Systems with Applications39 (2012), pp. 77767787Search in Google Scholar

2 C.Basheer, U. A.Dabade, S. J.Suhas, V. V.Bhanuprasad: Modelling of surface roughness in precision machining of metal matrix composites using ANN, Journal of Materials Processing Technology197 (2008), pp. 439444Search in Google Scholar

3 V. S.Sharma, S.Dhiman, R.Sehgal, S. K.Sharma: Estimation of cutting forces and surface roughness for hard turning using neural networks, Journal of Intelligent Manufacturing19 (2008), pp. 473483Search in Google Scholar

4 Y.Karpat, T.Özel: Multi-objective optimization for turning processes using neural network modelling and dynamic neighborhood particle swarm optimization, International Journal of Advanced Manufacturing Technology, 35 (2008), pp. 234247Search in Google Scholar

5 P. G.Benardos, G. C.Vosniakos: Prediction of surface roughness in CNC in machining: A review, International Journal of Machine Tools and Manufacture43 (2003), pp. 833844Search in Google Scholar

6 T.Özel, Y.Karpat: Predictive modelling of surface roughness and tool wear in hard turning using regression and neural networks, International Journal of Machine Tools and Manufacture45 (2005), pp. 467479Search in Google Scholar

7 Y.Kazancoglu, U.Esme, M.Bayramoglu, O.Guven, S.Ozgun, Multi-objective optimization of the cutting forces in turning operations using the Grey-based Taguchi method, Materiali in Tehnologije45 (2011), pp. 105110Search in Google Scholar

8 W. H.Yang, Y. S.Tarng: Design Optimization of cutting parameters for turning operations based on the Taguchi method84 (1988), pp. 122129Search in Google Scholar

9 D. C.Montgomery: Design and analysis of experiments, 7th Ed., Wiley, New York, USA (2009)Search in Google Scholar

10 P.Ross: Taguchi techniques for quality engineering, McGraw Hill, New York, USA (1991).Search in Google Scholar

11 P. L. B.Oxley: Modelling machining processes with a view to their optimization and the adaptive control of metal cutting machine tools, Robot. Comput.-Integrated Manuf.4 (1988), pp. 10311910.1016/0736-5845(88)90065-8Search in Google Scholar

12 G.Chryssolouris, M.Guillot: A comparison of statistical and AI approaches to the selection of process parameters in intelligent machining, ASME J. Eng. Ind.112 (1990), pp. 122131Search in Google Scholar

13 Y.Yao, X. D.Fang: Modelling of multivariate time series for tool wear estimation in finish turning, Int. J. Mach. Tools Manuf.32 (1992), No. 4, pp. 495508Search in Google Scholar

14 C.Zhou, R. A.Wysk: An integrated system for selecting optimum cutting speeds and tool replacement times, Int. J. Mach. Tools Manuf.32 (1992), No. 5, pp. 695707Search in Google Scholar

15 M. S.Chua, M.Rahman, Y. S.Wong, H. T.Loh: Determination of optimal cutting conditions using design of experiments and optimization techniques, Int. J. Mach. Tools Manuf.33 (1993), No. 2, pp. 297305Search in Google Scholar

16 A.Bendell, J.Disney, W. A.Pridmore: Taguchi Methods: Applications in World Industry, IFS Publications, UK (1989)Search in Google Scholar

17 D. C.Montgomery: Design and Analysis of Experiments, Wiley, Singapore (1991)Search in Google Scholar

18 S.Datta, A.Bandyopadhyay, P. K.Pal: Grey-based Taguchi method for optimization of bead geometry in submerged arc bead-on-plate welding, Int. J. Adv. Manuf. Technol.39 (2008), No. 11, pp. 11361143Search in Google Scholar

19 U.Esme, M.Bayramoglu, Y.Kazancoglu, S.Özgun: Optimization of weld bead geometry in TIG welding process using Grey relation analysis and Taguchi method, Materiali in Tehnologije43 (2009), pp. 143149Search in Google Scholar

20 D. S.Holmes, A. E.Mergen: Signal to Noise Ratio - What is the Right Size,, USA, (1996), pp. 1–6Search in Google Scholar

Published Online: 2013-05-26
Published in Print: 2012-11-01

© 2012, Carl Hanser Verlag, München

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