research-article

A multi-objective optimization of the friction stir welding process using RSM-based-desirability function approach for joining aluminum alloy 6063-T6 pipes

Authors:

R. Parameshwaran,

S. Ragu Nathan,

M. Bhuvanesh Kumar,

K. DeepanduraiAuthors Info & Claims

Structural and Multidisciplinary Optimization, Volume 62, Issue 3

Pages 1117 - 1133

https://doi.org/10.1007/s00158-020-02542-2

Published: 01 September 2020 Publication History

Abstract

In this study, a multi-objective optimization technique involving response surface methodology (RSM)-based desirability function approach is used in optimizing the process parameters for friction stir welding of AA6063-T6 pipes. Two process parameters, namely, tool rotational speed and weld speed, are optimized for achieving a weld joint having superior tensile properties, viz., maximum yield, and ultimate tensile strength and maximum % of elongation. A regression model, with a 95% confidence level, is developed using response surface methodology to predict the tensile strength of the weld joint. ANOVA technique is used to determine the adequacy of the developed model and identify the significant terms. The desirability function is used to analyze the responses and predict the optimal process parameters. It is found that tool rotational speed and weld speed have equal influence over the tensile strength of the pipe weld. Tool rotational speed 1986 rpm and weld speed 0.65 rpm have yielded a maximum ultimate tensile strength of 167 MPa, yield strength of 145 MPa, and % elongation of 8.3, under considered operating conditions. Microstructural attributes for superior weld properties are also discussed.

References

[1]

Agrawal AK, Narayanan RG, and Kailas SV End forming behaviour of friction stir processed Al 6063-T6 tubes at different tool rotational speeds J Strain Anal Eng Design 2017 52 434-449

[2]

Arbegast WJ (2003) Modeling friction stir joining as a metalworking process Proceedings of Hot Deformation of Aluminum Alloys III:313–327

[3]

Aydin H, Bayram A, Esme U, Kazancoglu Y, and Guven O Application of Grey relation analysis (GRA) and Taguchi method for the parametric optimization of friction stir welding (FSW) process Mater Technol 2010 44 205-211

[4]

Babu KK, Panneerselvam K, Sathiya P, Haq AN, Sundarrajan S, Mastanaiah P, Murthy CS (2017) Parameter optimization of friction stir welding of cryorolled AA2219 alloy using artificial neural network modeling with genetic algorithm. Int J Adv Manuf Technol:1–13

[5]

Bayazid S, Farhangi H, Ghahramani A (2015) Investigation of friction stir welding parameters of 6063-7075 aluminum alloys by Taguchi method. Procedia Mater Sci 11:6–11

[6]

Chouhan D, Pal SK, and Garg S Experimental study on the effect of welding parameters and tool pin profiles on mechanical properties of the FSW joints Dimensions 2013 806 28

[7]

Commin L, Dumont M, Masse J-E, and Barrallier L Friction stir welding of AZ31 magnesium alloy rolled sheets: influence of processing parameters Acta Mater 2009 57 326-334

[8]

Derringer G and Suich R Simultaneous optimization of several response variables J Qual Technol 1980 12 214-219

[9]

Doos QM (2012) Bashar, Abdul wahab Experimental study of friction stir welding of 6061-T6 aluminium pipe. Int J Mech Eng Robot 1

[10]

Elangovan K and Balasubramanian V Influences of pin profile and rotational speed of the tool on the formation of friction stir processing zone in AA2219 aluminium alloy Mater Sci Eng A 2007 459 7-18

[11]

Elangovan K and Balasubramanian V Influences of tool pin profile and welding speed on the formation of friction stir processing zone in AA2219 aluminium alloy J Mater Process Technol 2008 200 163-175

[12]

Gadakh VS and Adepu K Heat generation model for taper cylindrical pin profile in FSW J Mater Res Technol 2013 2 370-375

[13]

Ganapathy T, Lenin K, Pannerselvam K (2017) Process parameters optimization of friction stir welding in aluminium alloy 6063-T6 by Taguchi Method. In: Applied Mechanics and Materials. Trans Tech Publ, pp 97–104

[14]

Ghaffarpour M, Aziz A, Hejazi T-H (2017) Optimization of friction stir welding parameters using multiple response surface methodology. Proceedings of the Institution of Mechanical Engineers Part L: Journal of Materials: Design and Applications 231:571–583

[15]

Gupta SK, Pandey K, Kumar R (2018) Multi-objective optimization of friction stir welding process parameters for joining of dissimilar AA5083/AA6063 aluminum alloys using hybrid approach. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications 232:343353

[16]

Ismail A, Awang M, Fawad H, Ahmad K (2013) Friction stir welding on aluminum alloy 6063 pipe. In: Proceedings of the 7th Asia Pacific IIW International Congress, Singapore. pp 78–81

[17]

Kadaganchi R, Gankidi MR, and Gokhale H Optimization of process parameters of aluminum alloy AA 2014-T6 friction stir welds by response surface methodology Defence Technology 2015 11 209-219

[18]

Kalavathy MH, Regupathi I, Pillai MG, Miranda LR (2009) Modelling, analysis and optimization of adsorption parameters for H3PO4 activated rubber wood sawdust using response surface methodology (RSM) colloids and surfaces B: biointerfaces 70:35-45. 10.1016/j.colsurfb.2008.12.007

[19]

Khourshid A and Sabry I Friction stir welding study on aluminum pipe Int J Mech Eng Robot Res 2013 2 331-339

[20]

Lammlein D, Gibson B, DeLapp D, Cox C, Strauss A, and Cook G The friction stir welding of small-diameter pipe: an experimental and numerical proof of concept for automation and manufacturing Proc Inst Mech Eng B J Eng Manuf 2012 226 383-398

[21]

Mallieswaran K, Padmanabhan R, Balasubramanian V (2018) Friction stir welding parameters optimization for tailored welded blank sheets of AA1100 with AA6061 dissimilar alloy using response surface methodology. Advance Mater Proces Technol:1–16

[22]

Mehta KP, Badheka VJ (2016) Effects of tilt angle on the properties of dissimilar friction stir welding copper to aluminum Materials and Manufacturing processes 31:255–263

[23]

Mohapatra T, Sahoo SS, and Padhi BN Analysis, prediction and multi-response optimization of heat transfer characteristics of a three fluid heat exchanger using response surface methodology and desirability function approach Appl Therm Eng 2019 151 536-555

[24]

Moreira P, De Oliveira F, and De Castro P Fatigue behaviour of notched specimens of friction stir welded aluminium alloy 6063-T6 J Mater Process Technol 2008 207 283-292

[25]

Myers RH, Montgomery DC, Anderson-Cook CM (2016) Response surface methodology: process and product optimization using designed experiments. John Wiley & Sons

[26]

Padmanaban G and Balasubramanian V Selection of FSW tool pin profile, shoulder diameter and material for joining AZ31B magnesium alloy–an experimental approach Mater Des 2009 30 2647-2656

[27]

Pandiyarajan R, Maran P, Murugan N, Marimuthu S, and Sornakumar T Friction stir welding of hybrid AA 6061-ZrO2-C composites FSW process optimization using desirability approach Mater Res Express 2019 6 066553

[28]

Ramanujam R, Maiyar LM, and KVM V Multi response optimization using ANOVA and desirability function analysis: a case study in end milling of Inconel alloy ARPN J Eng Appl Sci 2014 9 457-463

[29]

Sahu PK, Kumari K, Pal S, and Pal SK Hybrid fuzzy-grey-Taguchi based multi weld quality optimization of Al/Cu dissimilar friction stir welded joints Adv Manuf 2016 4 237-247

[30]

Sashank JS, Sampath P, Krishna PS, Sagar R, Venukumar S, and Muthukumaran S Effects of friction stir welding on microstructure and mechanical properties of 6063 aluminium alloy Materials Today: Proceedings 2018 5 8348-8353

[31]

Selaimia A-A, Yallese MA, Bensouilah H, Meddour I, Khattabi R, and Mabrouki T Modeling and optimization in dry face milling of X2CrNi18-9 austenitic stainless steel using RMS and desirability approach Measurement 2017 107 53-67

[32]

Srivastava M, Maheshwari S, Kundra TK, Rathee S (2017) Multi-response optimization of fused deposition modelling process parameters of ABS using response surface methodology (RSM)-based desirability analysis materials today: Proceedings 4:1972-1977 10.1016/j.matpr.2017.02.043

[33]

Su H, Wu C, Pittner A, and Rethmeier M Simultaneous measurement of tool torque, traverse force and axial force in friction stir welding J Manuf Process 2013 15 495-500

[34]

Sudhagar S, Sakthivel M, Mathew PJ, and Daniel SAA A multi criteria decision making approach for process improvement in friction stir welding of aluminium alloy Measurement 2017 108 1-8

[35]

Verma S, Gupta M, and Misra JP Optimization of process parameters in friction stir welding of armor-marine grade aluminium alloy using desirability approach Mater Res Express 2018 6 026505

[36]

Vijay S and Murugan N Influence of tool pin profile on the metallurgical and mechanical properties of friction stir welded Al–10wt.% TiB 2 metal matrix composite Mater Des 2010 31 3585-3589

[37]

Wakchaure K, Thakur A, Gadakh V, and Kumar A Multi-objective optimization of friction stir welding of Aluminium alloy 6082-T6 using hybrid Taguchi-Grey relation analysis-ANN Method Materials Today: Proceedings 2018 5 7150-7159

[38]

Wang F, Li W, Shen J, Hu S, and dos Santos J Effect of tool rotational speed on the microstructure and mechanical properties of bobbin tool friction stir welding of Al–Li alloy Mater Des 2015 86 933-940

Cited By

Sangwan KKumar RHerrmann CSharma DPatel R(2023)Development of a cyber physical production system framework for 3D printing analytics▪Applied Soft Computing10.1016/j.asoc.2023.110719146:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110719
Nguyen TNguyen CVan A(2023)Sustainability-based optimization of dissimilar friction stir welding parameters in terms of energy saving, product quality, and cost-effectivenessNeural Computing and Applications10.1007/s00521-022-07898-835:7(5221-5249)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1007/s00521-022-07898-8

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A multi-objective optimization of the friction stir welding process using RSM-based-desirability function approach for joining aluminum alloy 6063-T6 pipes

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Published In

cover image Structural and Multidisciplinary Optimization

Structural and Multidisciplinary Optimization Volume 62, Issue 3

Sep 2020

550 pages

ISSN:1615-147X

Issue’s Table of Contents

© Springer-Verlag GmbH Germany, part of Springer Nature 2020.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 September 2020

Accepted: 10 February 2020

Revision received: 08 December 2019

Received: 10 October 2019

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Cited By

Sangwan KKumar RHerrmann CSharma DPatel R(2023)Development of a cyber physical production system framework for 3D printing analytics▪Applied Soft Computing10.1016/j.asoc.2023.110719146:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110719
Nguyen TNguyen CVan A(2023)Sustainability-based optimization of dissimilar friction stir welding parameters in terms of energy saving, product quality, and cost-effectivenessNeural Computing and Applications10.1007/s00521-022-07898-835:7(5221-5249)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1007/s00521-022-07898-8

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