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Identification of Process Parameters and Optimization Techniques for AA 6061 in FSW: State-of-the-art

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Abstract

Friction Stir Welding (FSW) is a new method of solid state joining of metals and nonmetals as a substitute technology applied in high strength alloys that are challenging in joining processes in traditional ways. At this contemporary epoch, many transportation industries utilize friction stir welding by its light weight higher strength weld properties. However, many problems are associated and diminution on the weld quality by a shortage of skills. One of the key challenges is selecting an appropriate optimization techniques and process parameters for single and multiple response studies. The current scenario, focused on the determination and identification of appropriate process parameters and optimization techniques for welding of AA6061 material using friction stir welding. All process parameters and optimization methods are intensively studied from the previous kinds of literature and identified appropriate process parameters for AA6061 materials. Based on the results, process parameters namely rotational speed at 43.7%, traverse speed at 17.29%, tool tilt angle 7.46%, axial force of 7.09%, ratio of tool shoulder-to-pin size 3.69%, other parameters are 1.73% contributions for achieving higher mechanical properties (tensile and hardness) of AA6061.

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Reference

  1. Ma, Z., Feng, A., Chen, D., Shen, J.: Recent advances in friction stir welding/processing of aluminum alloys: microstructural evolution and mechanical properties. J. Crit. Rev. Solid State 43, 269–333 (2018)

    Article  Google Scholar 

  2. Bosneag, A., Constantin, M., Nitu, E., Iordache, M.: Friction stir welding of three dissimilar aluminium alloy: AA2024, AA6061 and AA7075. In: IOP Conference Series: Materials Science and Engineering, p. 022013. IOP Publishing (2018)

    Google Scholar 

  3. Saravanakumar, R., Krishna, K., Rajasekaran, T., Siranjeevi, S.: Investigations on friction stir welding of AA5083-H32 marine grade aluminium alloy by the effect of varying the process parameters. In: IOP Conference Series: Materials Science and Engineering, p. 012187. IOP Publishing (2018)

    Google Scholar 

  4. Elatharasan, G., Kumar, V.S.: An experimental analysis and optimization of process parameter on friction stir welding of AA 6061–T6 aluminum alloy using RSM. Proc. Eng. 64, 1227–1234 (2013)

    Article  Google Scholar 

  5. Boukraa, M., Lebaal, N., Mataoui, A., Settar, A., Aissani, M., Tala-Ighil, N.: Friction stir welding process improvement through coupling an optimization procedure and three-dimensional transient heat transfer numerical analysis. J. Manuf. Process. 34, 566–578 (2018)

    Article  Google Scholar 

  6. Zhang, C., Wang, W., Jin, X., Rong, C., Qin, Z.: A study on microstructure and mechanical properties of micro friction stir welded ultra-Thin Al-1060 sheets by the shoulderless tool. J. Metals 9, 507 (2019)

    Article  Google Scholar 

  7. Kumar, A., Milton, M.S.: A Comparison of welding techniques of aluminium alloys a literature review. J. Int. J. Sci. Res. Sci. Eng. 2, 172–175 (2016)

    Google Scholar 

  8. Kumar, S., Kumar, S., Kumar, A.: Optimization of process parameters for friction stir welding of joining A6061 and A6082 alloys by Taguchi method. J. Mech. Eng. Sci. 227, 1150–1163 (2013)

    Article  Google Scholar 

  9. Roldo, L., Vulić, N.: Friction stir welding for marine applications: mechanical behaviour and microstructural characteristics of Al-Mg-Si-Cu plates. J. Trans. Maritime Sci. 8, 75–83 (2019)

    Article  Google Scholar 

  10. Dawood, H.I., Mohammed, K.S., Rajab, M.Y.: Advantages of the green solid state FSW over the conventional GMAW process. J. Adv. Mater. Sci. Eng. 2014, 1–10 (2014)

    Article  Google Scholar 

  11. Shaik, B., Gowd, G.H., Durgaprasad, B.: Experimental investigations on friction stir welding process to join aluminum alloys. Int. J. Appl. Eng. Res. 13, 12331–12339 (2018)

    Google Scholar 

  12. Kundu, J., Ghangas, G., Rattan, N., Kumar, M.: Friction stir welding: merits over other joining processes. Int. J. Curr. Eng. Technol. 7, 1175–1177 (2017)

    Google Scholar 

  13. Materials ASoT. Standard Test Methods for Tension Testing of Metallic Materials1. ASME Designation: E 8 – 042004, p. 1–24

    Google Scholar 

  14. Dixit, D., Mishra, A.: Friction stir welding of aerospace alloys. Int. J. Res. Appl. Sci. Eng. Technol. 7, 863–870 (2019)

    Article  Google Scholar 

  15. Kulekci, M.K.: Magnesium and its alloys applications in automotive industry. Int. J. Adv. Manuf. Technol. 39, 851–865 (2008)

    Article  Google Scholar 

  16. Roy, R.K.: A Primer on the Taguchi Method. 2nd edn. United States of America (2010)

    Google Scholar 

  17. Sagar Patel, P.K.M., Mirani, M.: A review- friction stir welding of AA6061 aluminum alloy using drilling machine. IJLTEMAS III, 33–37 (2014)

    Google Scholar 

  18. Nourani, M., Milani, A.S., Yannacopoulos, S.: Taguchi optimization of process parameters in friction stir welding of 6061 aluminum alloy: a review and case study. J. Eng. 3, 144–155 (2011)

    Google Scholar 

  19. Prasath, S., Vijayan, S., Rao, S.K.: Optimization of friction stir welding process parameters for joining ZM 21 to AZ 31 of dissimilar magnesium alloys using Taguchi technique. Metallurgia Italiana 25–33 (2016)

    Google Scholar 

  20. Ugender, S.: Influence of tool pin profile and rotational speed on the formation of friction stir welding zone in AZ31 magnesium alloy. J. Magnesium Alloys 6, 205–213 (2018)

    Article  Google Scholar 

  21. Mendes, N., Loureiro, A., Martins, C., Neto, P., Pires, J.: Effect of friction stir welding parameters on morphology and strength of acrylonitrile butadiene styrene plate welds. J. Mater. Design 58, 457–464 (2014)

    Article  Google Scholar 

  22. Sreenivas, P., Kumar, A.: Effect of applied axial force on Fsw of AA 6082–T6 aluminium alloys. Int. J. Mech. Eng. Technol. 8, 88–99 (2017)

    Google Scholar 

  23. Serier, M., Berrahou, M., Tabti, A., Bendaoudi, S.-E.: Effect of FSW welding parameters on the tensile strength of aluminum alloys. J. Arch. Mech. Technol. Mater. 39, 41–45 (2019)

    Article  Google Scholar 

  24. Jambhale, S., Kumar, S., Kumar, S.: Effect of process parameters & tool geometries on properties of friction stir spot welds: a review. Univ. J. Eng. Sci. 3, 6–11 (2015)

    Article  Google Scholar 

  25. Ugender, S., Jayakrishna, S., Francis, E.D.: Influence of welding speed, axial force and rotational speed on the formation of friction stir welding zone in AZ31 magnesium alloy. Int. J. Mech. Eng. Technol. 9, 845–857 (2018)

    Google Scholar 

  26. Ko, Y.-J., Lee, K.-J., Baik, K.-H.: Effect of tool rotational speed on mechanical properties and microstructure of friction stir welding joints within Ti–6Al–4V alloy sheets. J. Adv. Mech. Eng. 9, 1–7 (2017)

    Google Scholar 

  27. Iqbal, Z., Bazoune, A., Al-Badour, F., Shuaib, A., Merah, N.: Effect of tool rotational speed on friction stir welding of ASTM A516–70 steel using W–25% re alloy tool. Arab. J. Sci. Eng. 44, 1233–1242 (2019)

    Article  Google Scholar 

  28. Barlas, Z.: The Influence of tool tilt angle on 1050 aluminum lap joint in friction stir welding process. Acta Phys. Polonica A 132, 679–681 (2017)

    Article  Google Scholar 

  29. Krishna, G.G., Reddy, P.R., Hussain, M.M.: Effect of Tool tilt angle on aluminum 2014 friction stir welds. Glob. J. Res. Eng. 14, 60–70 (2015)

    Google Scholar 

  30. Murugan, B., Thirunavukarasu, G., Kundu, S., Kailas, S.V.: Influence of tool traverse speed on structure, mechanical properties, fracture behavior, and weld corrosion of friction stir welded joints of aluminum and stainless steel. J. Adv. Eng. Mater. 21, 1800869 (2019)

    Article  Google Scholar 

  31. Barenji, R.V.: Effect of tool traverse speed on microstructure and mechanical performance of friction stir welded 7020 aluminum alloy. J. Mater.: Design Appl. 230, 663–673 (2016)

    Google Scholar 

  32. Mohanty, H.K., Mahapatra, M.M., Kumar, P., Biswas, P., Mandal, N.R.: Effect of tool shoulder and pin probe profiles on friction stirred aluminum welds—a comparative study. J. Marine Sci. Appl. 11, 200–217 (2012)

    Article  Google Scholar 

  33. Moradi, M., Jamshidi Aval, H., Jamaati, R.: Effect of tool pin geometry and weld pass number on microstructural, natural aging and mechanical behaviour of SiC-incorporated dissimilar friction-stir-welded aluminium alloys. Indian Acad. Sci.44(1), 1–9 (2018). https://doi.org/10.1007/s12046-018-0997-5

    Article  Google Scholar 

  34. Khan, N.Z., Siddiquee, A.N., Al-Ahmari, A.M., Abidi, M.H.: Analysis of defects in clean fabrication process of friction stir welding. Trans. Nonferrous Metals Soc. China 27, 1507–1516 (2017)

    Article  Google Scholar 

  35. Meilinger, Á., Török, I.: The importance of friction stir welding tool. Prod. Process. Syst. 6, 25–34 (2013)

    Google Scholar 

  36. Venkateswarlu, D., Mandal, N., Mahapatra, M., Harsh, S.: Tool design effects for FSW of AA7039. Weld. J. 92, 41–47 (2013)

    Google Scholar 

  37. Said, M.T.S.M.: The effect of pin size on friction stir welded AA5083 Plate lap joint. In: International Conference on Production, Automobiles and Mechanical Engineering, pp. 87–92 (2015)

    Google Scholar 

  38. Khan, N.Z., Khan, Z.A., Siddiquee, A.N.: Effect of shoulder diameter to pin diameter (D/d) ratio on tensile strength of friction stir welded 6063 aluminium alloy. Mater. Today: Proc. 2, 1450–1457 (2015)

    Google Scholar 

  39. Joshi, S.K., Gandhi, J.D.: Influence of tool shoulder geometry on friction stir welding: a literature review. IJRSI III, 261–264 (2015)

    Google Scholar 

  40. Durakovic, B.: Design of experiments application, concepts, examples: State of the art. Period. Eng. Nat. Sci. 5, 421–439 (2017)

    Google Scholar 

  41. Sharma, G.V.S.S., Rao, R.U., Rao, P.S.: A Taguchi approach on optimal process control parameters for HDPE pipe extrusion process. J. Ind. Eng. Int. 13, 215–228 (2017)

    Article  Google Scholar 

  42. Fukuda, I.M., Pinto, C.F.F., Saviano, A.M., Lourenço, F.R., Moreira, C.D.S.: Design of experiments (DoE) applied to pharmaceutical and analytical Quality by Design (QbD). Braz. J. Pharmaceut. Sci. 54 (2018)

    Google Scholar 

  43. Kasman, Ş: Optimisation of dissimilar friction stir welding parameters with grey relational analysis. Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf. 227, 1317–1324 (2013)

    Article  Google Scholar 

  44. Amit Kumar, M.K.K., Singh, G.: Modeling and optimization of friction stir welding process parameters for dissimilar aluminium alloys. In: IConAMMA_2017, pp. 25440–25449. Materials Today, India (2018)

    Google Scholar 

  45. Ghetiya, N.D., Patel, K.M., Kavar, A.J.: Multi-objective optimization of FSW process parameters of aluminium alloy using taguchi-based grey relational analysis. Trans. Indian Inst. Met. 69, 917–923 (2015)

    Article  Google Scholar 

  46. Khaze, S.R., Masdari, M., Hojjatkhah, S.: Application of artificial neural networks in estimating participation in elections. Int. J. Inf. Technol. Model. Comput. 1, 23–31 (2013)

    Google Scholar 

  47. Manickam, M.V., Mohanapriya, M., Patil, S.P.: Research study on applications of artificial neural networks and E-learning personalization. Int. J. Civ. Eng. Technol. 8, 1422–1432 (2017)

    Google Scholar 

  48. Ihme, M., Marsden, A., Pitsch, H.: On the optimization of artificial neural networks for application to the approximation of chemical systems. J. Cent. Turbulence Res. Ann. Res. Briefs 105–118 (2006)

    Google Scholar 

  49. Arabzadeh, V., Niaki, S.T.A., Arabzadeh, V.: Construction cost estimation of spherical storage tanks: artificial neural networks and hybrid regression—GA algorithms. J. Ind. Eng. Int. 14(4), 747–756 (2017). https://doi.org/10.1007/s40092-017-0240-8

    Article  Google Scholar 

  50. Tuntas, R., Dikici, B.: An investigation on the aging responses and corrosion behaviour of A356/SiC composites by neural network: The effect of cold working ratio. J. Compos. Mater. 50, 2323–2335 (2016)

    Article  Google Scholar 

  51. Haldurai, L., Madhubala, T., Rajalakshmi, R.: A Study on genetic algorithm and its applications. Int. J. Comput. Sci. Eng. 4, 139–143 (2016)

    Google Scholar 

  52. Kristiadi, D., Hartanto, R.: Genetic algorithm for lecturing schedule optimization (case study: university of Boyolali). Indones. J. Comput. Cybern. Syst. 13, 83–94 (2019)

    Article  Google Scholar 

  53. Donoriyanto, D., Anam, A.: Application of genetic algorithm method on machine maintenance. J. Phys.: Conf. Ser. 012225 (2018)

    Google Scholar 

  54. Hussain, A., Muhammad, Y.S., Nawaz, A.: Optimization through genetic algorithm with a new and efficient crossover operator. Int. J. Adv. Math. 2018, 1–14 (2018)

    Google Scholar 

  55. Sobey, A.J., Grudniewski, P.A.: Re-inspiring the genetic algorithm with multi-level selection theory: multi-level selection genetic algorithm. J. Bioinspir. biomimet. 13, 1–14 (2018)

    Google Scholar 

  56. Hou, S., Wen, H., Feng, S., Wang, H., Li, Z.: Application of layered coding genetic algorithm in optimization of unequal area production facilities layout. J. Comput. Intell. Neurosci. 2019, 1–18 (2019)

    Google Scholar 

  57. Lai, Y., Dai, Y., Bai, X., Chen, D.: Discrete variable structural optimization based on multidirectional fuzzy genetic algorithm. Chin. J. Mech. Eng. 25, 255–261 (2012)

    Article  Google Scholar 

  58. Mohammadi, F.G., Amini, M.H., Arabnia, H.R.: Evolutionary computation, optimization and learning algorithms for data science. arXiv preprint arXiv (2019)

    Google Scholar 

  59. Chande, S., Sinha, M.: Genetic algorithm: a versatile optimization tool. BVICAM’s Int. J. Inf. Technol. 1, 7–13 (2013)

    Google Scholar 

  60. Lin, S.-T.: Application of grey-relational analysis to find the most suitable watermarking scheme. Int. J. Innov. Comput. Inf. Control 7, 5389–5401 (2011)

    Google Scholar 

  61. Vijayan, S., Raju, R., Rao, S.K.: Multiobjective optimization of friction stir welding process parameters on aluminum alloy AA 5083 using Taguchi-based grey relation analysis. J. Mater. Manuf. Process. 25, 1206–1212 (2010)

    Article  Google Scholar 

  62. Wang, L., Yin, K., Cao, Y., Li, X.: A new grey relational analysis model based on the characteristic of inscribed core (IC-GRA) and its application on seven-pilot carbon trading markets of China. Int. J. Environ. Res. Public Health 16, 1–16 (2019)

    Google Scholar 

  63. Kumar, A., Soota, T., Kumar, J.: Optimisation of wire-cut EDM process parameter by Grey-based response surface methodology. J. Ind. Eng. Int. 14(4), 821–829 (2018). https://doi.org/10.1007/s40092-018-0264-8

    Article  Google Scholar 

  64. Hrairi, M., Daoud, J.I., Zakaria, F.: Optimization of incremental sheet metal forming process using grey relational analysis. Int. J. Recent Technol. Eng. 7 (2019)

    Google Scholar 

  65. Shivade, A.S., Shinde, V.D.: Multi-objective optimization in WEDM of D3 tool steel using integrated approach of Taguchi method & Grey relational analysis. J. Ind. Eng. Int. 10, 149–162 (2014)

    Article  Google Scholar 

  66. Fang, G., Guo, Y., Huang, X., Rutten, M., Yuan, Y.: Combining grey relational analysis and a Bayesian model averaging method to derive monthly optimal operating rules for a hydropower reservoir. J. Water 10, 1–20 (2018)

    Google Scholar 

  67. Karthikeyan, R., Senthilkumar, V., Thilak, M., Nagadeepan, A.: Application of grey relational analysis for optimization of kerf quality during CO2 laser cutting of mild steel. J. Mater. Today: Proc. 5, 19209–19215 (2018)

    Google Scholar 

  68. Liu, C.-Y., Tong, L.-I.: Developing automatic form and design system using integrated grey relational analysis and affective engineering. J. Appl. Sci. 8, 1–22 (2018)

    Google Scholar 

  69. Khanna, R., Kumar, A., Garg, M.P., Singh, A., Sharma, N.: Multiple performance characteristics optimization for Al 7075 on electric discharge drilling by Taguchi grey relational theory. J. Ind. Eng. Int. 11, 459–472 (2015)

    Article  Google Scholar 

  70. Nair, A.T., Makwana, A.R., Ahammed, M.M.: The use of response surface methodology for modelling and analysis of water and wastewater treatment processes: a review. J. Water Sci. Technol. 69, 464–478 (2013)

    Article  Google Scholar 

  71. Raleng, A., Singh, A., Singh, B., Attkan, A.K.: Response surface methodology for development and characterization of extruded snack developed from food-by-products. Int. J. Bio-Resour. Stress Manage. 7, 1321–1329 (2016)

    Article  Google Scholar 

  72. Said, K.A.M., Amin, M.A.M.: Overview on the response surface methodology (RSM) in extraction processes. J. Appl. Sci. Process Eng. 2, 8–18 (2015)

    Google Scholar 

  73. Wang, Y., Deng, L., Fan, Y.: Preparation of soy-based adhesive enhanced by waterborne polyurethane: optimization by response surface methodology. J. Adv. Mater. Sci. Eng. 2018, 1–8 (2018)

    Article  Google Scholar 

  74. Bal, M., Biswas, S., Behera, S.K., Meikap, B., Health, P.A.: Modeling and optimization of process variables for HCl gas removal by response surface methodology. J. Environ. Sci. Health 54, 359–366 (2019)

    Article  Google Scholar 

  75. Akçay, H., Anagün, A.S.: Multi response optimization application on a manufacturing factory. J. Math. Comput. Appl. 18, 531–538 (2013)

    Google Scholar 

  76. Ramakrishna, G., Susmita, M.: Application of response surface methodology for optimization of Cr (III) and Cr (VI) adsorption on commercial activated carbons. Res. J. Chem. Sci. 4, 40–48 (2012)

    Google Scholar 

  77. Riswanto, F.D.O., Rohman, A., Pramono, S., Martono, S.: Application of response surface methodology as mathematical and statistical tools in natural product research. J. Appl. Pharmac. Sci. 9, 125–133 (2019)

    Article  Google Scholar 

  78. Dar, A.A., Anuradha, N.: An application of Taguchi L9 method in black scholes model for european call option. Int. J. Entrep. 22, 1–13 (2018)

    Google Scholar 

  79. Ishrat, S.I., et al.: Optimising parameters for expanded polystyrene based pod production using taguchi method. J. Math. 7, 1–17 (2019)

    Google Scholar 

  80. Yılmaz, M., Keskin, M.E.: Optimal Okuma Şartlarının Taguchi Yöntemiyle Belirlenmesi. Acad. Platform J. Eng. Sci. 7, 25–32 (2019)

    Google Scholar 

  81. Baligidad, S.M., Chandrasekhar, U., Elangovan, K., Shankar, S.: Taguchi’s Approach: Design optimization of process parameters in selective inhibition sintering. J. Mater. Today: Proc. 5, 4778–4786 (2018)

    Google Scholar 

  82. Li, Y., Shieh, M.-D., Yang, C.-C., Zhu, L.: Application of fuzzy-based hybrid Taguchi method for multiobjective optimization of product form design. J. Math. Probl. Eng. 2018, 1–18 (2018)

    Google Scholar 

  83. Achuthamenon Sylajakumari, P., Ramakrishnasamy, R., Palaniappan, G.: Taguchi grey relational analysis for multi-response optimization of wear in co-continuous composite. J. Mater. 11, 1–17 (2018)

    Google Scholar 

  84. Azadeh, A., Miri-Nargesi, S.S., Goldansaz, S.M., Zoraghi, N.: Design and implementation of an integrated Taguchi method for continuous assessment and improvement of manufacturing systems. Int. J. Adv. Manuf. Technol. 59, 1073–1089 (2012)

    Article  Google Scholar 

  85. Qadir, S., Dar, A.A.: Distance to default and probability of default: an experimental study. J. Glob. Entrep. Res. 9, 1–12 (2019)

    Google Scholar 

  86. Reddy, A., Rajesham, S., Reddy, P., Kumar, T., Goverdhan, J.: An experimental study on effect of process parameters in deep drawing using Taguchi technique. Int. J. Eng. Sci. Technol. 7, 21–32 (2015)

    Article  Google Scholar 

  87. Vaibhav Khola, H.R., Masudi, M.: Optimization of process parameters on Inconel 718 using Taguchi’s technique. Int. Res. J. Eng. Technol. 5, 1272–1279 (2018)

    Google Scholar 

  88. Shunmugasundaram, M., Kumar, A.P., Sankar, L.P., Sivasankar, S.: Optimization of process parameters of friction stirs welding of aluminum alloys (6061) using Taguchi method. Int. J. Sci. Res. 5, 1988–1994 (2016)

    Google Scholar 

  89. Borkar, B.R., Navale, S.B.: Process parameters optimization in FSW process using Taguchi method. IJARIIE 4, 551–558 (2018)

    Google Scholar 

  90. Ugender, S.: Optimizing the process parameters of friction stir welded AA 6061–T6 alloy using Taguchi orthogonal technique. Int. J. Curr. Eng. Sci. Res. 1, 48–55 (2014)

    Google Scholar 

  91. Gupta, S.K., Pandey, K., Kumar, R.: Multi-objective optimization of friction stir welding of aluminium alloy using grey relation analysis with entropy measurement method. Nirma Univ. J. Eng. Technol. (NUJET). 3, 29–34 (2015)

    Google Scholar 

  92. Gomathisankar, M., Gangatharan, M., Pitchipoo, P.: A Novel optimization of friction stir welding process parameters on aluminum alloy 6061–T6. Mater. Today: Proc. 5, 14397–14404 (2018)

    Google Scholar 

  93. Kumar, S., Pandey, G.K.N.: Application of Taguchi method for optimization of friction stir welding process parameters to joining of Al alloy. Adv. Mater. Manuf. Charact. 13, 253–258 (2013)

    Google Scholar 

  94. Prasad, M.D., Kumar Namala, K.: Process parameters optimization in friction stir welding by ANOVA. Mater. Today: Proc. 5, 4824–4831 (2018)

    Google Scholar 

  95. Ugrasen, G., Bharath, G., Kumar, G.K., Sagar, R., Shivu, P., Keshavamurthy, R.: Optimization of process parameters for Al6061-Al7075 alloys in friction stir welding using Taguchi’s technique. Mater. Today: Proc. 5, 3027–3035 (2018)

    Google Scholar 

  96. Vijayan, D., Rao, V.S.: Optimization of friction stir welding process parameters using RSM based Grey-Fuzzy approach. J. Eng. Technol. 2, 12–25 (2017)

    Google Scholar 

  97. Surjeet Singh, K.S., Singh, I., Shivesh, C.: An experimental analysis and optimization of process parameters on friction stir welding of dissimilar AA6061-T6 and AA6951-T6 using taguchi technique. Int. Res. J. Eng. Technol. 04, 3329–3235 (2017)

    Google Scholar 

  98. Kumar, P.R., Raj, R.G.: A review on friction stir weldment of AA6061 and AA1100 aluminium alloys. Int. J. Adv. Inf. Sci. Technol. 3, 104–108 (2014)

    Google Scholar 

  99. Devaiah, D., Kishore, K., Laxminarayana, P.: Study the process parametric influence on impact strength of friction stir welding of dissimilar aluminum alloys (AA5083 and AA6061) using Taguchi technique. Inte. Adv. Res. J. Sci. Eng. Technol. 3, 91–98 (2016)

    Google Scholar 

  100. Devaiah, D., Kishore, K., Laxminarayana, P.: Parametric optimization of friction stir welding parameters using taguchi technique for dissimilar aluminum alloys (AA5083 and AA6061). Int. Organ. Sci. Res. 7, 44–49 (2017)

    Google Scholar 

  101. Chaitanya, V.K., Varma, S.R., Raju, P.R.M., Viswanadha Raju, V.K.: Influence of welding parameters on the mechanical properties of dissimilar AA7075-AA6061 friction stir welds. Int. J. Recent Technol. Eng. 8, 81–88 (2019)

    Google Scholar 

  102. Hema, P., Raviteja, N., Ravindranath, K.: Prediction and parametric optimization on mechanical properties of friction stir welding joints of AA 6061 and AA 2014 using genetic algorithm. Int. J. Innov. Res. Sci. Eng. Technol. 5, 3870–3877 (2016)

    Google Scholar 

  103. Devaiah, D., Kishore, K., Laxminarayana, P.: Optimization of process parameters in friction stir welding of dissimilar aluminium alloys (AA5083 and AA6061) using Taguchi technique. Int. J. Innov. Res. Sci. Eng. Technol. 5, 15303–15310 (2016)

    Google Scholar 

  104. Bahar, D., Arvind, N., Yadav, V.V., Raju, P.: Multi objective optimization in friction stir welding using Taguchi orthogonal array and grey relational analysis. Int. J. Adv. Technol. Eng. Explor. 5, 214–220 (2018)

    Article  Google Scholar 

  105. Vijayan, D., Rao, V.S.: Friction stir welding of age-hardenable aluminum alloys: a parametric approach using RSM based GRA coupled with PCA. J Inst. Eng. India Ser. 95, 127–141 (2014)

    Article  Google Scholar 

  106. Devaiah, D., Kishore, K., Laxminarayana, P.: Optimal FSW process parameters for dissimilar aluminium alloys (AA5083 and AA6061) using Taguchi tech46nique. J. Mater. Today: Proc. 5, 4607–4614 (2018)

    Google Scholar 

  107. Chanakyan, C., Sivasankar, S., Alagarsamy, S.V., Kumar, S.D., Sakthivelu, S.: Parametric optimization for friction stir welding with AA2024 and AA6061 aluminium alloys by ANOVA and GRG. Mater. Today 27, 1–5 (2019)

    Google Scholar 

  108. Ugender, S., Ma: Taguchi optimization of process parameters in friction stir welding of aluminium 2014 & 6061 alloys. Int. J. Curr. Eng. Sci. Res. 2, 34–40 (2015)

    Google Scholar 

  109. Kumar, S., Kumar, S.: Multi-response optimization of process parameters for friction stir welding of joining dissimilar Al alloys by gray relation analysis and Taguchi method. J. Braz. Soc. Mech. Sci. Eng. 37, 1–10 (2014)

    Google Scholar 

  110. Hema, P.: Experimental investigations on AA 6061 alloy welded joints by friction stir welding. J. Aluminum Alloys Compos. (2019)

    Google Scholar 

  111. Chiteka, K.: Artificial neural networks in tensile strength and input parameter prediction in Friction Stir Welding. Int. J. Mech. Eng. Robot. Res. 03, 145–150 (2014)

    Google Scholar 

  112. Khourshid, A.M., El-Kassas, A.M., Sabry, I.: Integration between artificial neural network and responses surfaces methodology for modeling of friction stir welding. Int. J. Adv. Eng. Res. Sci. 2, 67–73 (2015)

    Google Scholar 

  113. Momeni, M., Guillot, M.: Effect of tool design and process parameters on lap joints made by right angle friction stir welding (RAFSW). J. Manuf. Mater. Process. 3, 1–14 (2019)

    Google Scholar 

  114. Vijayan, D., Abhishek, P.: Multi objective process parameters optimization of friction stir welding using NSGA–II. In: IOP Conference Series: Materials Science and Engineering, p. 012087. IOP Publishing (2018)

    Google Scholar 

  115. Sankar, B.R., Umamaheswarrao, P.: Optimisation of hardness and tensile strength of friction stir welded AA6061 alloy using response surface methodology coupled with grey relational analysis and principle component analysis. Int. J. Eng. Sci. Technol. 7, 21–29 (2015)

    Article  Google Scholar 

  116. Samuela, G.D., Dhasb, J.E.R.: Multi-Objective Optimization of friction stir welded dissimilar aluminium composites using grey analysis. Int. J. Appl. Eng. Res. 12, 1279–1289 (2017)

    Google Scholar 

  117. Vijayan, D., Seshagiri, R.: A parametric optimization of FSW process using RSM based grey relational analysis approach. Int. Rev. Mech. Eng. (IREME). 8, 328–337 (2014)

    Google Scholar 

  118. Prasanna, P., Penchalayya, C., Rao, D.: Optimization and validation of process parameters in friction stir welding on AA 6061 aluminum alloy using gray relational analysis. Int. J. Eng. Res. Appl. (IJERA). 3, 1471–1481 (2013)

    Google Scholar 

  119. Yunus, M., Alsoufi, M.S.: Multi-objective optimization of joint strength of dissimilar aluminum alloys formed by friction stir welding using Taguchi-grey relation analysis. Int. J. Eng. Technol. 6, 10–17 (2016)

    Google Scholar 

  120. Gopu, P., Dev Anand, M.: Optimal parameter determination on friction stir welding process of AA6061 using grey Taguchi method. Int. J. Recent Technol. Eng. 8, 46–50 (2019)

    Google Scholar 

  121. RaviKumar, S., KajaBanthaNavas, R., Sai, S.: Multiple response optimization studies for dissimilar friction stir welding parameters of 6061 to 7075 aluminium alloys. Mater. Today: Proc. 16, 405–412 (2019)

    Google Scholar 

  122. Dhancholia, D.D., Sharma, A., Vyas, C.: Optimisation of friction stir welding parameters for AA 6061 and AA 7039 aluminium alloys by response surface methodology (RSM). Int. J. Adv. Mech. Eng. 4, 565–571 (2014)

    Google Scholar 

  123. Kavitha, S., Rajkumar, S.: Identification of the most critical friction stir welding process and tool parameters to attain a maximum tensile strength of the AA6061-T6 aluminium alloy. Int. J. Res. Advent Technol. 128–136 (2018)

    Google Scholar 

  124. Iswar, M., Suyuti, M.A., Nur, R.: Optimizing the machining conditions on friction stir welding of aluminum alloy through design experiments. Innov. Sci. Technol. Mech. Eng. Ind. 030003, 1–5 (2019)

    Google Scholar 

  125. Sankar, B.R., Umamaheswarrao, P.: Modelling and optimisation of friction stir welding on AA6061 Alloy. Mater. Today: Proc. 4, 7448–7456 (2017)

    Google Scholar 

  126. Safeen, W., Hussain, S., Wasim, A., Jahanzaib, M., Aziz, H., Abdalla, H.: Predicting the tensile strength, impact toughness, and hardness of Friction Stir-Welded AA6061-T6 using response surface methodology. Int. J. Adv. Manuf. Technol. 87, 1765–1781 (2016)

    Article  Google Scholar 

  127. Ghaffarpour, M., Aziz, A., Hejazi, T.-H.: Optimization of friction stir welding parameters using multiple response surface methodology. J. Mater. Design Appl. 231, 571–583 (2017)

    Google Scholar 

  128. Elatharasan, G., Kumar, V.S.: Modelling and optimization of friction stir welding parameters for dissimilar aluminium alloys using RSM. Proc. Eng. 38, 3477–3481 (2012)

    Article  Google Scholar 

  129. Hanapi, M., Haslam, M., Hussain, Z., Almanar, I.P., Abu Seman, A.: Optimization processing parameter of 6061-T6 alloy friction stir welded using Taguchi technique. Mater. Sci. Forum: Trans. Tech. Publ. 294–298 (2016)

    Google Scholar 

  130. Anuradha, M., Sailaja, C, Chittaranjan Das, V.: Effect of tool pin profile and optimization of process parameters on A6061 by friction stir welding using Taguchi method. Int. J. Mech. Eng. Technol. 8, 615–621 (2017)

    Google Scholar 

  131. Chauhan, S.M.S.P.Y.B.: Optimization of friction stir welding process parameters for welding aluminum alloys. Int. J. Sci. Technol. Eng. 2, 69–75 (2015)

    Google Scholar 

  132. Harikishore, R., Satyavinod, L.: Parametric optimization for friction stir welding of Al6061 alloy using Taguchi technique. Int. J. Sci. Res. 6, 334–339 (2017)

    Google Scholar 

  133. Shinde, R.D., Rathi, M.G.: Optimization of FSW process parameter to achieve maximum tensile strength of aluminum alloy AA6061. Int. Res. J. Eng. Technol. 03, 936–943 (2016)

    Google Scholar 

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  1. Bahir Dar Institute of Technology, Bahir Dar, Ethiopia

    Eyob Messele Sefene & Assefa Asmare Tsegaw

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  1. Eyob Messele Sefene
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  2. Assefa Asmare Tsegaw
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Correspondence to Eyob Messele Sefene .

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  1. Bahir Dar University, Bahir Dar, Ethiopia

    Mulugeta Admasu Delele

  2. Bahir Dar University, Bahir Dar, Ethiopia

    Mekuanint Agegnehu Bitew

  3. Bahir Dar University, Bahir Dar, Ethiopia

    Abebech Abera Beyene

  4. Bahir Dar University, Bahir Dar, Ethiopia

    Solomon Workneh Fanta

  5. Bahir Dar University, Bahir Dar, Ethiopia

    Addisu Negash Ali

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Sefene, E.M., Tsegaw, A.A. (2021). Identification of Process Parameters and Optimization Techniques for AA 6061 in FSW: State-of-the-art. In: Delele, M.A., Bitew, M.A., Beyene, A.A., Fanta, S.W., Ali, A.N. (eds) Advances of Science and Technology. ICAST 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 385. Springer, Cham. https://doi.org/10.1007/978-3-030-80618-7_15

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