Optimization of friction stir welding process parameters using soft computing techniques

In welding processes, desired weld quality is highly dependent on the selection of optimal process conditions. In this work, the influence of input parameters of friction stir welding process is studied using Taguchi method and full factorial design of experiment. The experimental data set is used to develop multilayer feed-forward artificial neural network (ANN) models using back-propagation training algorithm. These models are used to predict weld qualities as a function of eight process parameters. The weld qualities of the welded joint, such as ultimate tensile strength, yield stress, percentage elongation, bending angle and hardness, are considered. In order to offline optimize these quality characteristics, four evolutionary algorithms, namely binary-coded genetic algorithm, real-coded genetic algorithm, differential evolution and particle swarm optimization, are coupled with the developed ANN models. The optimized quality characteristics obtained from these proposed techniques are compared and verified with experimental results.

[1]  Livan Fratini,et al.  Using a neural network for predicting the average grain size in friction stir welding processes , 2009 .

[2]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[3]  N. D. Ghetiya,et al.  Prediction of Tensile Strength in Friction Stir Welded Aluminium Alloy Using Artificial Neural Network , 2014 .

[4]  V. Balasubramanian,et al.  Comparison of RSM with ANN in predicting tensile strength of friction stir welded AA7039 aluminium alloy joints , 2009 .

[5]  Mohammad Hassan Shojaeefard,et al.  Multi objective optimization of friction stir welding parameters using FEM and neural network , 2014, International Journal of Precision Engineering and Manufacturing.

[6]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[7]  Erol Arcaklioğlu,et al.  Artificial neural network application to the friction stir welding of aluminum plates , 2007 .

[8]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[9]  Antonette M. Logar,et al.  The use of neural network and discrete Fourier transform for real-time evaluation of friction stir welding , 2011, Appl. Soft Comput..

[10]  Mostafa Akbari,et al.  Modelling and Pareto optimization of mechanical properties of friction stir welded AA7075/AA5083 butt joints using neural network and particle swarm algorithm , 2013 .

[11]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[12]  Cem Celal Tutum,et al.  A multi-objective optimization application in Friction Stir Welding: Considering thermo-mechanical aspects , 2010, IEEE Congress on Evolutionary Computation.

[13]  Fabrizio Micari,et al.  Mechanical and microstructural properties prediction by artificial neural networks in FSW processes of dual phase titanium alloys , 2012 .

[14]  Pedro Neto,et al.  Numerical modeling of friction stir welding process: a literature review , 2012, The International Journal of Advanced Manufacturing Technology.

[15]  Rajiv S. Mishra,et al.  Friction Stir Welding and Processing , 2007 .