Characterization of the tensile properties of friction stir welded aluminum alloy joints based on axial force, traverse speed, and rotational speed

doi:10.1007/s11465-016-0393-y

Front. Mech. Eng.

2016, Vol. 11

Issue (3) : 289-298 https://doi.org/10.1007/s11465-016-0393-y

RESEARCH ARTICLE

Characterization of the tensile properties of friction stir welded aluminum alloy joints based on axial force, traverse speed, and rotational speed

Biranchi PANDA¹,A. GARG^2,^*(

),Zhang JIAN²,Akbar HEIDARZADEH³,Liang GAO⁴

¹. IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisboa 1040-001, Portugal
². Department of Mechatronics Engineering, Shantou University, Shantou 515063, China
³. Department of Materials Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran
⁴. The State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China

Download: PDF(1393 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Friction stir welding (FSW) process has gained attention in recent years because of its advantages over the conventional fusion welding process. These advantages include the absence of heat formation in the affected zone and the absence of large distortion, porosity, oxidation, and cracking. Experimental investigations are necessary to understand the physical behavior that causes the high tensile strength of welded joints of different metals and alloys. Existing literature indicates that tensile properties exhibit strong dependence on the rotational speed, traverse speed, and axial force of the tool that was used. Therefore, this study introduces the experimental procedure for measuring tensile properties, namely, ultimate tensile strength (UTS) and tensile elongation of the welded AA 7020 Al alloy. Experimental findings suggest that a welded part with high UTS can be achieved at a lower heat input compared with the high heat input condition. A numerical approach based on genetic programming is employed to produce the functional relationships between tensile properties and the three inputs (rotational speed, traverse speed, and axial force) of the FSW process. The formulated models were validated based on the experimental data, using the statistical metrics. The effect of the three inputs on the tensile properties was investigated using 2D and 3D analyses. A high UTS was achieved, including a rotational speed of 1050 r/min and traverse speed of 95 mm/min. The results also indicate that 8 kN axial force should be set prior to the FSW process.

Keywords tensile properties ultimate tensile strength tensile elongation friction stir welding tool rotational speed genetic programming welding speed

Corresponding Author(s): A. GARG

Online First Date: 20 June 2016 Issue Date: 31 August 2016

Cite this article:

Biranchi PANDA,A. GARG,Zhang JIAN, et al. Characterization of the tensile properties of friction stir welded aluminum alloy joints based on axial force, traverse speed, and rotational speed[J]. Front. Mech. Eng., 2016, 11(3): 289-298.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-016-0393-y
https://academic.hep.com.cn/fme/EN/Y2016/V11/I3/289

Fig.1 Mechanism of the FSW process at the top and bottom sheets

Fig.2 Procedure of the experimental and numerical investigation of the tensile properties of FSW-welded joints

Tab.1 Inputs used for the experimental set-up of FSW

Tab.2 Uniform experiment design for the measurement of tensile properties

Fig.3 Line graph showing the nature of (a) ultimate tensile strength, and (b) tensile elongation

Fig.4 GP approach for modeling tensile properties

Tab.3 Statistical metrics of the models for two tensile properties

Tab.4 Actual and model values for two tensile properties obtained from the models

Fig.5 Curve fitting of the tensile properties models on the data set. (a) Ultimate tensile strength; (b) tensile elongation

Fig.6 2D plots showing the relationships of (a) ultimate tensile strength and rotational speed, (b) ultimate tensile strength and traverse speed, (c) ultimate tensile strength and axial force, and (d) tensile elongation and rotational speed

Fig.7 3D plots showing the relationships of ultimate tensile strength and (a) rotational speed and traverse speed, (b) traverse speed and axial force, and (c) rotational speed and axial force

Fig.8 3D plots showing the relationships of tensile elongation based on (a) rotational speed and axial force, (b) rotational speed and traverse speed

Fig.9 Percentage contribution of the three inputs to (a) ultimate tensile strength, and (b) tensile elongation

1	Yazdipour A, Heidarzadeh A. Effect of friction stir welding on microstructure and mechanical properties of dissimilar Al 5083-H321 and 316L stainless steel alloy joints. Journal of Alloys and Compounds, 2016, 680: 595–603 https://doi.org/10.1016/j.jallcom.2016.03.307
2	Yazdipour A, Heidarzadeh A. Dissimilar butt friction stir welding of Al 5083-H321 and 316L stainless steel alloys. The International Journal of Advanced Manufacturing Technology, 2016, 1–8
3	Heidarzadeh A, Khodaverdizadeh H, Mahmoudi A, Tensile behavior of friction stir welded AA 6061-T4 aluminum alloy joints. Materials & Design, 2012, 37: 166–173 https://doi.org/10.1016/j.matdes.2011.12.022
4	Heidarzadeh A, Kazemi-Choobi K, Hanifian H, 3-Microstructural evolution. In: Besharati-Givi M K, Asadi P, eds. Advances in Friction-Stir Welding and Processing. Woodhead Publishing, 2014, 65–140 https://doi.org/10.1533/9780857094551.65
5	Khodaverdizadeh H, Mahmoudi A, Heidarzadeh A, Effect of friction stir welding (FSW) parameters on strain hardening behavior of pure copper joints. Materials & Design, 2012, 35: 330–334 https://doi.org/10.1016/j.matdes.2011.09.058
6	Heidarzadeh A, Saeid T. A comparative study of microstructure and mechanical properties between friction stir welded single and double phase brass alloys. Materials Science and Engineering A, 2016, 649: 349–358 https://doi.org/10.1016/j.msea.2015.10.012
7	Heidarzadeh A, Saeid T. On the effect of β phase on the microstructure and mechanical properties of friction stir welded commercial brass alloys. Data in Brief, 2015, 5: 1022–1025 https://doi.org/10.1016/j.dib.2015.11.013
8	Heidarzadeh A, Saeid T. Correlation between process parameters, grain size and hardness of friction-stir-welded Cu-Zn alloys. Rare Metals, 2016, 1–11
9	Heidarzadeh A, Jabbari M, Esmaily M. Prediction of grain size and mechanical properties in friction stir welded pure copper joints using a thermal model. The International Journal of Advanced Manufacturing Technology, 2015, 77(9–12): 1819–1829 https://doi.org/10.1007/s00170-014-6543-7
10	Golezani A S, Barenji R V, Heidarzadeh A, Elucidating of tool rotational speed in friction stir welding of 7020-T6 aluminum alloy. International Journal of Advanced Manufacturing Technology, 2015, 81(5–8): 1155–1164 https://doi.org/10.1007/s00170-015-7252-6
11	Rahimzadeh Ilkhichi A, Soufi R, Hussain G, Establishing mathematical models to predict grain size and hardness of the friction stir-welded AA 7020 aluminum alloy joints. Metallurgical and Materials Transactions B, 2015, 46 (1): 357–365
12	Barenji R V. Influence of heat input conditions on microstructure evolution and mechanical properties of friction stir welded pure copper joints. Transactions of the Indian Institute of Metals, 2016, 69(5): 1077–1085
13	Garg A, Panda B, Shankhwar K. Investigation of the joint length of weldment of environmental-friendly magnetic pulse welding process. The International Journal of Advanced Manufacturing Technology, 2016, 1–12
14	Barenji R V. Effect of tool traverse speed on microstructure and mechanical performance of friction stir welded 7020 aluminum alloy. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials Design and Applications, 2015, 230(2): 1–11
15	Azizi A, Barenji R V, Barenji A V, Microstructure and mechanical properties of friction stir welded thick pure copper plates. The International Journal of Advanced Manufacturing Technology, 2016, 1–11
16	Sharma V, Prakash U, Kumar B M. Surface composites by friction stir processing: A review. Journal of Materials Processing Technology, 2015, 224: 117–134 https://doi.org/10.1016/j.jmatprotec.2015.04.019
17	Rajakumar S, Muralidharan C, Balasubramanian V. Establishing empirical relationships to predict grain size and tensile strength of friction stir welded AA 6061-T6 aluminium alloy joints. Transactions of Nonferrous Metals Society of China, 2010, 20(10): 1863–1872 https://doi.org/10.1016/S1003-6326(09)60387-3
18	Babu S, Elangovan K, Balasubramanian V, Optimizing friction stir welding parameters to maximize tensile strength of AA2219 aluminum alloy joints. Metals and Materials International, 2009, 15(2): 321–330 https://doi.org/10.1007/s12540-009-0321-3
19	Heidarzadeh A, Saeid T, Khodaverdizadeh H, Establishing a mathematical model to predict the tensile strength of friction stir welded pure copper joints. Metallurgical and Materials Transactions B, Process Metallurgy and Materials Processing Science, 2013, 44(1): 175–183 https://doi.org/10.1007/s11663-012-9755-y
20	Lakshminarayanan A K, Balasubramanian V. Comparison of RSM with ANN in predicting tensile strength of friction stir welded AA7039 aluminium alloy joints. Transactions of Nonferrous Metals Society of China, 2009, 19(1): 9–18 https://doi.org/10.1016/S1003-6326(08)60221-6
21	Mohammadzadeh A, Azadbeh M, Namini S A. Densification and volumetric change during supersolidus liquid phase sintering of prealloyed brass Cu28Zn powder: modeling and optimization. Science of Sintering, 2014, 46(1): 23–35 https://doi.org/10.2298/SOS1401023M
22	Zhao D, Tian Q, Li Z, A new stepwise and piecewise optimization approach for CO 2 pipeline. International Journal of Greenhouse Gas Control, 2016, 49: 192–200 https://doi.org/10.1016/j.ijggc.2016.03.005
23	Zhao D, Zhu Q, Dubbeldam J. Terminal sliding mode control for continuous stirred tank reactor. Chemical Engineering Research & Design, 2015, 94: 266–274 https://doi.org/10.1016/j.cherd.2014.08.005
24	Zhao D, Ni W, Zhu Q. A framework of neural networks based consensus control for multiple robotic manipulators. Neurocomputing, 2014, 140: 8–18 https://doi.org/10.1016/j.neucom.2014.03.041
25	Zhao D, Zhu Q, Li N, Synchronized control with neuro-agents for leader—Follower based multiple robotic manipulators. Neurocomputing, 2014, 124: 149–161 https://doi.org/10.1016/j.neucom.2013.07.016
26	Vijayaraghavan V, Garg A, Wong C H, An integrated computational approach for determining the elastic properties of boron nitride nanotubes. International Journal of Mechanics and Materials in Design, 2015, 11(1): 1–14 https://doi.org/10.1007/s10999-014-9262-1
27	Vijayaraghavan V, Castagne S. Computational model for predicting the effect of process parameters on surface characteristics of mass finished components. Engineering Computations, 2016, 33(3): 789–805 https://doi.org/10.1108/EC-04-2015-0094
28	Garg A, Vijayaraghavan V, Wong C H, Combined CI-MD approach in formulation of engineering moduli of single layer graphene sheet. Simulation Modelling Practice and Theory, 2014, 48: 93–111 https://doi.org/10.1016/j.simpat.2014.07.008
29	Vijayaraghavan V, Castagne S. Sustainable manufacturing models for mass finishing process. The International Journal of Advanced Manufacturing Technology, 2015, 1–9
30	Vijayaraghavan V, Wong C H. Torsional characteristics of single walled carbon nanotube with water interactions by using molecular dynamics simulation. Nano-Micro Letters, 2014, 6(3): 268–279 https://doi.org/10.1007/BF03353791
31	Wong C H, Vijayaraghavan V. Nanomechanics of imperfectly straight single walled carbon nanotubes under axial compression by using molecular dynamics simulation. Computational Materials Science, 2012, 53(1): 268–277 https://doi.org/10.1016/j.commatsci.2011.08.011
32	Panda B N, Bahubalendruni M R, Biswal B B. A general regression neural network approach for the evaluation of compressive strength of FDM prototypes. Neural Computing & Applications, 2015, 26(5): 1129–1136 https://doi.org/10.1007/s00521-014-1788-5
33	Panda B N, Bahubalendruni M R, Biswal B B. Comparative evaluation of optimization algorithms at training of genetic programming for tensile strength prediction of FDM processed part. Procedia Materials Science, 2014, 5: 2250–2257 https://doi.org/10.1016/j.mspro.2014.07.441
34	Panda B N, Babhubalendruni M R, Biswal B B, Application of artificial intelligence methods to spot welding of commercial aluminum sheets (B.S. 1050). In: Das K N, Deep K, Pant M, et al. eds. Proceedings of Fourth International Conference on Soft Computing for Problem Solving. Springer, 2015, 21–32
35	Vijayaraghavan V, Garg A, Lam J S L, Process characterisation of 3D-printed FDM components using improved evolutionary computational approach. International Journal of Advanced Manufacturing Technology, 2015, 78(5–8): 781–793 https://doi.org/10.1007/s00170-014-6679-5
36	Garg A, Vijayaraghavan V, Wong C H, An embedded simulation approach for modeling the thermal conductivity of 2D nanoscale material. Simulation Modelling Practice and Theory, 2014, 44: 1–13 https://doi.org/10.1016/j.simpat.2014.02.003
37	Garg A, Tai K. Comparison of regression analysis, artificial neural network and genetic programming in handling the multicollinearity problem. In: Proceedings of 2012 International Conference on Modelling, Identification & Control (ICMIC). Wuhan: IEEE, 2012, 353–358
38	Asghari A, Gandomi A H. Ductility reduction factor and collapse mechanism evaluation of a new steel knee braced frame. Structure and Infrastructure Engineering: Maintenance, Management, Life-Cycle Design and Performance, 2016, 12(2): 239–255 https://doi.org/10.1080/15732479.2015.1009123
39	Gandomi A H, Faramarzifar A, Rezaee P G, New design equations for elastic modulus of concrete using multi expression programming. Journal of Civil Engineering and Management, 2015, 21(6): 761–774 https://doi.org/10.3846/13923730.2014.893910
40	Heidarzadeh A, Barenji R V, Esmaily M, Tensile properties of friction stir welds of AA 7020 aluminum alloy. Transactions of the Indian Institute of Metals, 2015, 68(5): 757–767 https://doi.org/10.1007/s12666-014-0508-2
41	Koza J R. Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge: MIT press, 1992
42	Vapnik V N. Statistical Learning Theory. New York: Wiley, 1998
43	Garg A, Lam J S L, Gao L. Energy conservation in manufacturing operations: Modelling the milling process by a new complexity-based evolutionary approach. Journal of Cleaner Production, 2015, 108: 34–45 https://doi.org/10.1016/j.jclepro.2015.06.043
44	Searson D P, Leahy D E, Willis M J. GPTIPS: An open source genetic programming toolbox for multigene symbolic regression. In: Proceedings of the International MultiConference of Engineers and Computer Scientists. Hong Kong: Newswood Ltd., 2010, 1: 77–80

Viewed

Full text

Abstract

Cited

Shared

Discussed