Authors:
Lam Suvarna Raju,Venu Borigorla,DOI NO:
https://doi.org/10.26782/jmcms.2020.07.00042Keywords:
FSW,Process Parameters, Mechanical Properties,Genetic Algorithm,TLBO,Abstract
AA2014 has been extensively used in manufacture of light weight fabricated components similar to commercial automobile components, which requires high strength with minimal weight and along with decent corrosion effect. The traditional welding of thisAluminium alloyed materials generally encounter solidification problems like hot cracking. Friction Stir Welding (FSW) is an ecofriendly joining process where in the actual melting of material and recasting will not happen. Many of the researchers carried out sufficient experiments for optimizing process parameters and to establish empirical relationships in order to predict better mechanical properties. In the present investigation, a comparative study of FSW between experimentation and optimization of process parameters such as tool rotation speed and weld speed, to attain maximum mechanical properties using Genetic Algorithm (GA) and Teaching Learning Based Optimization (TLBO) algorithm. From the results it shows that the TLBO gives the better combinations of process parameters which give superior mechanical properties compared to experimental results as well as other optimization techniques.Refference:
I. W,M,Thomas, E,D,Nicholas, J,C,Needham, M,G, Murch, P,Temple Smith, and C,J,Dawas, Int.Patent Appl.No.PCT/GB92/02203 and GB patent Appl:No 9125978.8, Dec1991, U.S.Patent Appl.No.5460317, Oct 1995.
II. R,Nandan, T,DebRoy, and H,K,D,H,Bhadeshia, “Recent Advances in Friction-Stir Welding:process, weldment structure and properties”, Prog.Mater.Sci., vol.53, pp.980-1023, 2008
III. R, S, Mishra and Z, Y,Ma, “Friction Stir Welding and Processing”, Mater. Sci. Eng. R, vol.50, pp.1-78, 2005.
IV. Anton Savio Lewise, K and Edwin Raja Dhas,J, “A Review of Friction Stir Welding of Aluminium alloys”, International journal of Advanced Chemical Science and applications, vol.5, no.3, pp.28-32, 2017.
V. Thirupathireddy, G, Syed Rabbani Bash, “Effect of weld speed on tool pin profile using friction stir welding”, IJSRM, vol.3, no.1, pp.1892-1896, 2015.
VI. Indira Rani, M, Marpu, RN and Kumar, ACS, “A study of process parameters of friction stir welded AA6061 aluminum alloy in O and T6 conditions”, ARPN J EngAppl Sci, vol.6, pp.2006–2011, 2011.
VII. Khalid Hussain, A and Pasha Quadri, S, “A evaluations of parameters of friction stir welding for aluminum AA6351 alloy”, Int J Eng Sci Technol, vol.2, pp.5977–5984, 2010.
VIII. MortezaGhaffarpour, Ahmad Aziz and Taha-Hossein Hejazi, “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, vol.231, no.7, pp.571–583, 2017.
IX. Ghaffarpour, M, MollaeiDariani, B and Kokabi, AH et al, “Friction stir welding parameters optimization of heterogeneous tailored welded blank sheets of aluminium alloys 6061 and 5083 using response surface methodology”,J EngManuf, vol.44 no.A, pp.2013–2022, 2012.
X. Yousif, YK, Daws, KM and Kazem, BI, “Prediction of friction stir welding characteristic using neural network”, Jordan J Mech Ind Eng, vol.2, pp.151–155, 2008.
XI. Vidal, C, Infante, V, and Pec¸as P, et al, “Assessment of improvement techniques effect on fatigue behavior of friction stir welded aerospace aluminium alloys” Procedia Eng, vol.2, pp.1605–1616, 2010.
XII. Shahrabi, J and Hejazi, TH, “A new mathematical program based on principal component analysis for multiple response optimization”, IEEE International Conference on Quality and Reliability, vol.42, pp. 445–450, 2011.
XIII. Venkata Rao, R, Kalyankar, VD, multi-pass turning process parameter optimization using teaching-learned-based optimization algorithm, Scientia Iranica E, vol.20, no.3, pp. 967-974,2013.
XIV. Venkata Rao,K, Murthy PBGSN and Vidhu KP, “Assignment of weightage to machining characteristics to improve overall performance of machining using GTMA and utility concept”. CIRP, journal Manufacturing Science and Technology, vol. 18, pp.152–158,2017.
XV. Cheema, MS, Dvivedi, A, and Sharma, AK, “A Hybrid approach to multicriteria optimization based on user’s preference rating”. Proceesings of I Mech E Part B: Journal of Engineering Manufacture, vol.227, no.11, pp.1733-1742, 2013.
XVI. Kadaganchi,R, Gankidi,M.R and Gokhale,H, “Optimization of process parameters of aluminum alloy AA 2014-T6 friction stir welds by response surface methodology”. Def. Technol, vol.11, pp.209–219, 2015.
XVII. Kumar, A and Suvarna Raju, L, “Influence of Tool Pin Profiles on Friction Stir Welding of Copper”. Materials and Manufacturing Processes, vol.27, no.12, pp.1414-1418, 2012.