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Licensed Unlicensed Requires Authentication Published by De Gruyter March 29, 2016

Tensile shear strength and elongation of FSW parts predicted by Taguchi-based fuzzy logic

Zugscherfestigkeit und Verlängerung von rührreibgeschweißten Teilen vorhergesagt mittels Taguchi-basierter Fuzzy-Logik
  • Mustafa Kemal Kulekci , Ugur Esme , Seref Ocalir , Deniz Ustun and Yigit Kazancoglu
From the journal Materials Testing


This paper represents the fuzzy logic model for modeling and prediction of tensile shear strength and percent elongation of parts produced by the friction stir welding (FSW) process. A Taguchi L16 orthogonal array is used to plan and select the parameters and their levels. Weld travel speed, pin diameter and tool rotation are used as input variables. Therefore, a three-input and two-output fuzzy model is used to correlate these variables to the responses of tensile shear strength and percent elongation using the fuzzy rules generated based on experimental results. Close agreement is obtained between the fuzzy predicted and experimental results with the correlation coefficients of 0.931 and 0.895 for tensile shear strength and elongation, respectively.


In dem vorliegenden Beitrag wird ein Modell basierend auf Fuzzy-Logik vorgestellt, mit dem die Scherzugfestigkeit und die prozentuale Verlängerung von Teilen modelliert und vorhergesagt werden kann, die mit dem Rührreibschweißprozess (Friction Stir Welding (FSW)) hergestellt wurden. Hierzu wurde ein L16 orthogonales Taguchi-Array verwendet, um die Parameter und ihre Werte zu planen und auszuwählen. Die Schweißgeschwindigkeit, der Pin-Durchmesser und die Werkzeugrotation wurden als Inputvariablen ausgewählt. Daher wurde ein Fuzzy-Modell mit drei Input-Parametern und drei Output-Parametern und ihren entsprechenden Werten verwendet, um diese Variablen mit den Antworten der Scherzugfestigkeit und der prozentualen Verlängerungen zu korrelieren, wobei die Fuzzy-Regeln verwendet wurden, die basierend auf experimentellen Ergebnissen ermittelt wurden. Es ergab sich eine enge Übereinstimmung der mit Fuzzy-Logik vorhergesagten mit den experimentellen Ergebnissen mit den Korrelationskoeffizienten von 0.931 für die Scherzugfestigkeit bzw. 0.895 für die Verlängerung.

*Correspondence Address, Associate Prof. Dr. Ugur Esme, Department of Manufacturing Engineering, Engineering Faculty, Mersin University, Institute of Natural and Applied Sciences, 33400, Tarsus-Mersin/Turkey. E-mail:

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

Assoc. Prof. Dr. Ugur Esme is working at Mersin University, Tarsus Technical Education Faculty, Turkey. He obtained his PhD degree from the Department of Mechanical Engineering of Cukurova University, Turkey, in 2006. His research areas include CAD/CAM technology, welding, modeling, designing and water jet cutting applications.

Seref Ocalir, born in 1982, works as a research assistant at the Graduate School of Natural and Applied Sciences, Mersin University, Turkey. He obtained his BSc degree and MSc degree from Mersin University, Turkey, in 2006 and in 2009, respectively. His research interests include friction stir welding, machinability of materials, design and corrosion.

Deniz Ustun has completed his BS degree in the Department of Computer Science Engineering of Istanbul University, Turkey in 2001. He received his MSc degree in Electrical and Electronics Engineering from Mersin University, Turkey in 2009. Since 2010, he has been working on his PhD degree in the same department. His current research interests are artificial neural networks and computer modeling of heuristic optimization algorithms.

Yigit Kazancoglu is Associate Professor İn the Department of Business Administration, Izmir University of Economics, Turkey. He received his BS degree from the Industrial Engineering Department of Eastern Mediterranean University, Famagusta, North Cyprus, Turkey, his MBA degree from Coventry Univertsity, UK, and Izmir University of Economics, Turkey, and PhD degree in Operations Management from Ege University, Izmir, Turkey. 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 R. E.Dolby, A.Sanderson, P. L.Threadgill, U.Dilthey: Recent developments and applications in electron beam and friction technologies, Proc. of the 7th International Aachen Welding Conference, Shaker Verlag, Germany (2001), pp. 5973Search in Google Scholar

2 H.Okuyucu, A.Kurt, E.Arcaklioglu: Artificial neural network application to the friction stir welding of aluminum plates, Materials and Design28 (2007), pp. 788410.1016/j.matdes.2005.06.003Search in Google Scholar

3 U.Esme, M. K.Kulekci, Y.Kazancoglu: The use of artificial neural networks in predicting fatigue life of friction stir welded lap joints of AA 5754, Journal of Advanced Materials42 (2010), pp. 1421Search in Google Scholar

4 Q.Yang, S.Mironov, Y. S.Sato, K.Okamoto: Material flow during friction stir spot welding, Materials Science and Engineering A527 (2010), pp. 4389439810.1016/j.msea.2010.03.082Search in Google Scholar

5 G.Çam, S.Güçlüer, A.Çakan, H. T.Serindag: Mechanical properties of friction stir butt-welded Al-5086 H32 plate, Materialwissenschaften und Werkstofftechnik40 (2009), pp. 63864210.1002/mawe.200800455Search in Google Scholar

6 H.Badarinarayan, Y.Shi, X.Li, K.Okamoto: Effect of tool geometry on hook formation and static strength of friction stir spot welded aluminum 5754-O sheet, International Journal of Machine Tools and Manufacture49 (2009), pp. 81482310.1016/j.ijmachtools.2009.06.001Search in Google Scholar

7 P.Cavaliere, A.De Santis, F.Panella, A.Squillace: Effect of welding parameters on mechanical and microstructural properties of dissimilar AA6082–AA2024 joints produced by friction stir welding, Materials & Design30 (2009), pp. 60961610.1016/j.matdes.2008.05.044Search in Google Scholar

8 A. A.Zadpoor, J.Sinke, R.Benedictus, R.Pieters: Mechanical properties and microstructure of friction stir welded tailor-made blanks, Materials Science and Engineering494 (2008), pp. 28129010.1016/j.msea.2008.04.042Search in Google Scholar

9 N.Afrin, D. L.Chen, X.Cao, M.Jahazi: Microstructure and tensile properties of friction stir welded AZ31B magnesium alloy, Materials Science and Engineering A472 (2008), pp. 17918610.1016/j.msea.2007.03.018Search in Google Scholar

10 C.Zhou, X.Yang, G.Luan: Investigation of microstructures and fatigue properties of friction stir welded Al–Mg alloy, Materials Chemistry and Physics98 (2006), pp. 28529010.1016/j.matchemphys.2005.09.019Search in Google Scholar

11 M.Ericson, R.Sandstrom: Influence of welding speed on the fatigue of friction stir welds, and comparison with MIG and TIG, International Journal of Fatigue25 (2002), pp. 1379138710.1016/S0142-1123(03)00059-8Search in Google Scholar

12 M. A.Gharacheh, A. H.Kokabi, G. H.Daneshi, B.Shalchi, R.Sarrafi: The influence of the rotational speed/traverse speed (w/v) on mechanical properties of AZ31 friction stir welds, International Journal Machine Tools Manufacture46 (2006), pp. 1983198710.1016/j.ijmachtools.2006.01.007Search in Google Scholar

13 S.Ocalir: Determination of Optimum Friction Stir Welding Parameters During the Welding of Aluminum Alloy, MSc Thesis, Mersin University Institute of Natural and Applied Sciences, Mersin, Turkey (2009)Search in Google Scholar

14 S.Datta, A.Bandyopadhyay, P. K.Pal: Grey-based Taguchi method for optimization of bead geometry in submerged arc bead-on-plate welding, International Journal of Advanced Manufacturing Technology39 (2008), pp. 1136114310.1007/s00170-007-1283-6Search in Google Scholar

15 B.Buldum, U.Eşme, M. K.Külekci, A.Şik, Y.Kazançoğlu: Use of Grey-Taguchi method for the optimization of oblique turning process of AZ91D magnesium alloy, Materials Testing54 (2012), pp. 77978510.3139/120.110392Search in Google Scholar

16 V.Novák, I.Perfilieva, J.Močkoř: Mathematical Principles of Fuzzy Logic, Kluwer Academic, Netherlands (1999)10.1007/978-1-4615-5217-8Search in Google Scholar

17 in Google Scholar

18 L. A.Zadeh: Fuzzy sets, Information and Control8 (1965), pp. 33835310.1016/S0019-9958(65)90241-XSearch in Google Scholar

19 Y. M.Ali, L. C.Zhang: Surface roughness prediction of ground components using a fuzzy logic approach, Journal of Materials Processing Technology89 (1999), pp. 56156810.1016/S0924-0136(99)00022-9Search in Google Scholar

20 L. A.Zadeh: Fuzzy logic, Computer83 (1988), pp. 839310.1109/2.53Search in Google Scholar

21 B.Kosko: Fuzzy Thinking, Harper Collins, London, UK (1994)Search in Google Scholar

22 B.Kosko: Neural Networks and Fuzzy Systems, Prentice Hall, New Jersey, USA (1992)Search in Google Scholar

23 J. L.Lina, K. S.Wang, B. H.Yan, Y. S.Tarng: Optimization of the electrical discharge machining process based on the Taguchi method with fuzzy logics, Journal of Materials Processing Technology102 (2000), pp. 485510.1016/S0924-0136(00)00438-6Search in Google Scholar

24 R. K.Pandey, S. S.Panda: Optimization of bone drilling parameters using grey-based fuzzy algorithm, Measurement47 (2014), pp. 38639210.1016/j.measurement.2013.09.007Search in Google Scholar

Published Online: 2016-03-29
Published in Print: 2016-04-04

© 2016, Carl Hanser Verlag, München

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