Title: Determination of an optimum parametric combination using a tensile strength prediction model for friction stir welded AA8011 aluminium alloy

Authors: N.D. Ghetiya; K.M. Patel; S.J. Makvana

Addresses: Department of Mechanical Engineering, Institute of Technology, Nirma University, Ahmedabad – 382481, Gujarat, India ' Department of Mechanical Engineering, Institute of Technology, Nirma University, Ahmedabad – 382481, Gujarat, India ' Miranda Tools, (Div. of PMP Auto Components Pvt. Ltd.), Plot No. 903-4, G.I.D.C. Industrial Estate, Ankleshwar-393002, Gujarat, India

Abstract: Friction stir welding is widely used for the welding of aluminium. The heat required for welding is produced using non-consumable tool. Welding input parameters play a vital role in determining the strength of joint and quality of a weld joint. In the present study, mathematical model has been developed using response surface method to predict strength of the friction stir welded AA8011 aluminium alloy. Four factors, five levels central composite design has been used to reduce the number of experimental conditions. Adequacy of the developed model has been checked by statistical tool analysis of variance and validated by Chi square test. Conformation experiments have been carried out to verify validity of the developed model. The developed mathematical model can predict the tensile strength of FSW joints. Results of the study indicate that the maximum tensile strength found in the FSW welded joint is 75% of the parent metal tensile strength. Genetic algorithm is used for the optimisation of the tensile strength. A calculator has also been developed using visual basic for calculation of tensile strength. [Received 1 January 2013; Revised 23 September 2013; Accepted 18 January 2014]

Keywords: parameter combinations; prediction modelling; aluminium alloys; friction stir welding; FSW; tensile strength; welding parameters; weld joints; joint strength; joint quality; response surface methodology; RSM; central composite design; CCD; analysis of variance; ANOVA; mathematical modelling; genetic algorithms; optimisation.

DOI: 10.1504/IJMR.2014.064437

International Journal of Manufacturing Research, 2014 Vol.9 No.3, pp.258 - 275

Published online: 30 Aug 2014 *

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