Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter October 1, 2014

Optimization of Friction Welding Parameters of AISI 904L Super Austenitic Stainless Steel by Evolutionary Computational Techniques

Optimierung von Reibschweißparametern eines AISI 904L superaustenitischen Stahls mit evolutionären Algorithmen
  • Karupanan Balamurugan , Jiju V Elies , Paul Sathiya and Abdullah Naveen Sait
From the journal Materials Testing


Friction welding is a type of solid state welding which plays an important role in joining metal surfaces with the help of frictional heat accompanied with high force. Optimization of process parameters of friction welding is important for all types of materials. Optimization of input parameters of friction welding also plays a very significant role in determining the quality of a weld joint. Purpose of this study is to optimize the welding process parameters in friction welding of AISI 904L super austenitic stainless steel by using regression analysis and evolutionary algorithms. This study is to determine optimimum welding process parameters of friction welding with the help of genetic algorithm (GA) and simulated annealing (SA). Also, it explains how to obtain near optimum welding conditions in a wide range by conducting a relatively small number of experiments. Results of these evolutionary computational techniques were compared with experimental results. Finally, an optimization parameter is obtained for a maximum fatigue life and a minimum welding time.


Reibschweißen ist eine Schweißart im festen Zustand, die eine große Bedeutung für die Verbindung von Metalloberflächen mittels Reibwärme begleitet von großen Kräften hat. Die Optimierung der Prozessparameter beim Reibschweißen ist für alle Materialarten wichtig. Die Optimierung der Eingangsparameter beim Reibschweißen ist maßgebend für die Bestimmung der Schweißnahtqualität. Der Zweck der diesem Beitrag zugrunde liegenden Studie besteht darin, die Prozessparameter beim Reibschweißen eines AISI 904L superaustenitischen Stahls mittels Regressionsanalyse und evolutionären Algorithmen zu optimieren. Diese Studie hat das Ziel, die optimalen Parameter mit Hilfe eines genetischen Algorithmus' (GA) und simuliertem Annealing (SA) zu bestimmen. Außerdem beschreibt sie, wie nahezu optimale Schweißbedingungen in einem großen Suchbereich unter Durchführung einer relativ kleinen Zahl von Experimenten erhalten werden. Die Ergebnisse dieser evolutionären Algorithmustechniken wurden mit experimentellen Resultaten verglichen. Schließlich wurde ein Optimierungsparameter für eine maximale Lebensdauer unter Ermüdungsbeanspruchung und einer minimalen Schweißzeit gefunden.

* Correspondence Address, Abdullah Naveen Sait Department of Mechanical Engineering, Chendhuran College of Engineering and Technology, Pudukkottai-622 507, Tamil Nadu, India, E-Mail:

Mr. Karupanan Balamurugan born on 01-10-1971, Completed M.E in the year 2002 and Bachelors in the year 1995 in Mechanical Engineering. Mr. Balamurugan is pursuing his PhD Programme in Periyar Maniammai University, Thanjavur, India.

Mr. Jiju V. Elies obtained his M. Tech in Manufacturing Engineerig from National Institute of Technology, Tiruchirappalli, Tamilnadu, India in the year 2011. Currently he is Working as graduate engineer for G.E, Bangalore, India.

Dr. Paul Sathiya born on 06-01-1973, obtained Ph.D in the year 2006, Masters during 1996 and Bachelors in the year 1994. Basically Dr. Sathiya is a mechanical Engineer doing extensive research in the field of Welding, Presently he is working with National Institute of Technology, Tiruchirappalli, Tamilnadu, India as Associate Professor in the department of Production Engineering.

Dr. Abdullah Naveen Sait born on 02-12-1976, obtained PhD in the year 2008, Masters during 2001 and Bachelors in the year 1998. Dr. Naveen Sait is carrying research in the field of materials. Presently he is working with Chendhuran College of Engineering and Technology, Pudukkottai, Tamilnadu, India as Principal.


1 K.Deb: Optimizations for Engineering Design – Algorithm and Examples, Prentice Hall, New Delhi, India (1996), pp. 290333Search in Google Scholar

2 I. S.Kim, K. J.Son, Y. S.Yang, P. K. D. V.Yaragada: Sensitivity analysis for process parameters in GMA welding processes using a factorial design method, International Journal of Machine Tools & Manufacture43 (2003), No. 8, pp. 76376910.1016/S0890-6955(03)00054-3Search in Google Scholar

3 K. G. K.Murti, S.Sundaresan: Parameter optimization in friction welding dissimilar materials, Metal Construction (1983), No. 6, pp. 331335Search in Google Scholar

4 S. C.Juang, Y. S.Tarang: Process parameter selection for optimizing the weld pool geometry in tungsten inert gas welding of stainless steel, Journal of Materials Processing Technology122 (2002), No. 1, pp. 333710.1016/S0924-0136(02)00021-3Search in Google Scholar

5 V.Gunaraj, N.Murugan: Application of response surface methodology for predicting weld bead quality in submerged arc welding of pipes, Journal of Material Processing Technology88 (1999), No. 1–3, pp. 26627510.1016/S0924-0136(98)00405-1Search in Google Scholar

6 K.Ill-Soo, S.Joon-Sik, K. D. V. PrasadYarlagadda: A study on the quality improvement of robotic GMA welding process, Robotics and Computer Integrated Manufacturing19 (2003), No. 6, pp. 56757210.1016/S0736-5845(03)00066-8Search in Google Scholar

7 Y. M.Zhang, R.Kovacevic: Characterization and real-time measurement of geometrical appearance of the weld pool, International Journal of Machine tools & Manufacture136 (1996), No. 7, pp. 79981610.1016/0890-6955(95)00083-6Search in Google Scholar

8 D.Kim, S.Rhee: Optimization of arc welding process parameters using a genetic algorithm, Welding Journal (2001), July, pp. 184189Search in Google Scholar

9 Y. S.Tarang, H. L.Tsai, S. S.Yeh: Modeling optimization and classification of weld quality in tungsten inert gas welding, International Journal of Machine Tools & Manufacture39 (1999), No. 9, pp. 1427143810.1016/S0890-6955(99)00013-9Search in Google Scholar

10 C. C.Tutum, D.Kalyanmoy, J. H.Hattel: Multi-criteria optimization in friction stir welding using a thermal model with prescribed material flow, Materials and Manufacturing Processes28 (2013), No. 7, pp. 81682210.1080/10426914.2012.736654Search in Google Scholar

11 B. K.Giri, F.Pettersson, H.Saxén, N.Chakraborti: Genetic programming evolved through bi-objective genetic algorithms applied to a blast furnace, Materials and Manufacturing Processes28 (2013), No. 7, pp. 77678210.1080/10426914.2013.763953Search in Google Scholar

12 W.Paszkowicz: Genetic algorithms, a nature-inspired tool: A survey of applications in materials science and related fields: Part II, Materials and Manufacturing Processes28 (2013), No. 7, pp. 70872510.1080/10426914.2012.746707Search in Google Scholar

13 P. A.Molian: Solidification behaviour of laser welded stainless steel, Journal of Materials Science Letters4 (1985), pp. 28128310.1007/BF00719791Search in Google Scholar

14 B.Leffler: Stainless Steels and their Properties, Avesta Polarit AB, Technical Bulletin, in Google Scholar

15 J. H.Holland: Adaptation in Natural and Artificial Systems, Ann Arbor, The University of Michigan Press, Chicago, USA (1975)Search in Google Scholar

16 A.Diabata, D.Kannan, M.Kaliyan, D.Svetinovice: An optimization model for product returns using genetic algorithms and artificial immune system, Resources, Conservation and Recycling74 (2013), pp. 15616910.1016/j.resconrec.2012.12.010Search in Google Scholar

17 G.Kannan, A. N.Haq, M.Devika: Analysis of closed loop supply chain using genetic algorithm and particle swarm optimization, International Journal of Production Research47 (2009), No. 5, pp. 1175120010.1016/j.resconrec.2012.12.010Search in Google Scholar

18 R.Paventhan, P. R.Lakshminarayanan, V.Balasubramanian: Optimization of friction welding process parameters for joining carbon steel and stainless steel, J. Iron. Steel Res, Int.19 (2012), No. 1, pp. 667110.1016/S1006-706X(12)60049-1Search in Google Scholar

19 M.Yousefieh, M.Shamanian, A.Saatchi: Optimization of the pulsed current gas tungsten arc welding (PCGTAW) parameters for corrosion resistance of super duplex stainless steel (UNS S32760) welds using the Taguchi method, J. Alloy Compd.509 (2010), No. 3,pp. 78278810.1016/j.jallcom.2010.09.087Search in Google Scholar

20 V.Gunaraj, N.Murugan: Application of response surface methodology for predicting weld bead quality in submerged arc welding of pipes, J. Mater. Process. Technol.88 (1998), No. 1–3, pp. 26627510.1016/S0924-0136(98)00405-1Search in Google Scholar

21 P.Sathiya, S.Aravindan, A. NoorulHaq, K.Paneerselvam: Optimization of friction welding parameters using evolutionary computational techniques, J. Mater. Process. Technol.209 (2009), No. 5, pp. 2576258410.1016/j.jmatprotec.2008.06.030Search in Google Scholar

22 S.Rajakumar, C.Muralidharan, V.Balasubramanian: Predicting tensile strength, hardness and corrosion rate of friction stir welded AA6061-T6 aluminium alloy joints, Materials and Design, 32 (2011), No. 5, pp. 2878289010.1016/j.matdes.2010.12.025Search in Google Scholar

23 P.Sathiya, K.Panneerselvam, M. Y. AbdulJaleel: Optimization of laser welding process parameters for super austenitic stainless steel using artificial neural networks and genetic algorithm, Materials and Design, 32 (2011), No. 36, pp. 1253126110.1016/j.matdes.2011.11.028Search in Google Scholar

24 A.Konak, D. W.Coit, A. E.Smith: Multi-objective optimization using genetic algorithms: a tutorial, Reliab. Eng. Syst. Safety, 91 (2006), pp. 992100710.1016/j.ress.2005.11.018Search in Google Scholar

Published Online: 2014-10-01
Published in Print: 2014-03-03

© 2014, Carl Hanser Verlag, München

Downloaded on 11.12.2023 from
Scroll to top button