Application of integrated soft computing techniques for optimisation of hybrid CO2 laser-MIG welding process

Three integrated ANN-GA, ANN-SA and ANN-Quasi Newton methodology has been developed and implemented according to the following way to determine optimised input parameter setting for maximum welding strength during laser-MIG hybrid welding of aluminium alloy plates. Finally, significance of optimised parameters has been determined by ANOVA. Variation of welding strength with individual process parameters have been tested through main effect plots and interaction plots. Three soft computing based integrated models such as, ANN-GA, ANN-SA and ANN-Quasi Newton have been developed.Those models predicted and optimised welding strength during hybrid CO2 laser-MIG welding process.Best ANN architecture (3-11-1 network) predicts welding strength with mean absolute percentage errors less than 2%.ANN-GA shows best optimisation performance with only 0.09% experimental validation error.Welding speed shows maximum influence on welding strength and an increase in welding speed decreases welding strength. In this paper, artificial neural networks (ANNs), genetic algorithm (GA), simulated annealing (SA) and Quasi Newton line search techniques have been combined to develop three integrated soft computing based models such as ANN-GA, ANN-SA and ANN-Quasi Newton for prediction modelling and optimisation of welding strength for hybrid CO2 laser-MIG welded joints of aluminium alloy. Experimental dataset employed for the purpose has been generated through full factorial experimental design. Laser power, welding speeds and wires feed rate are considered as controllable input parameters. These soft computing models employ a trained ANN for calculation of objective function value and thereby eliminate the need of closed form objective function. Among 11 tested networks, the ANN with best prediction performance produces maximum percentage error of only 3.21%. During optimisation ANN-GA is found to show best performance with absolute percentage error of only 0.09% during experimental validation. Low value of percentage error indicates efficacy of models. Welding speed has been found as most influencing factor for welding strength.

[1]  Young Whan Park,et al.  Process modeling and parameter optimization using neural network and genetic algorithms for aluminum laser welding automation , 2008 .

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

[3]  Xiaoyan Zeng,et al.  Study on microstructure and mechanical properties of 304 stainless steel joints by TIG, laser and laser-TIG hybrid welding , 2010 .

[4]  David J. C. MacKay,et al.  A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.

[5]  Bekir Sami Yilbas,et al.  Wedge cutting of mild steel by CO2 laser and cut-quality assessment in relation to normal cutting , 2008 .

[6]  Luca Tomesani,et al.  The influence of arc transfer mode in hybrid laser-mig welding , 2007 .

[7]  Chung-Feng Jeffrey Kuo,et al.  Application of an LM-neural network for establishing a prediction system of quality characteristics for the LGP manufactured by CO2 laser , 2011 .

[8]  Liming Liu,et al.  Fusion welding of Fe-added lap joints between AZ31B magnesium alloy and 6061 aluminum alloy by hybrid laser–tungsten inert gas welding technique , 2012 .

[9]  Chuen-Tsai Sun,et al.  Neuro-fuzzy And Soft Computing: A Computational Approach To Learning And Machine Intelligence [Books in Brief] , 1997, IEEE Transactions on Neural Networks.

[10]  Yanyan Zhu,et al.  A comparative study of laser beam welding and laser–MIG hybrid welding of Ti–Al–Zr–Fe titanium alloy , 2011 .

[11]  Olcay Ersel Canyurt,et al.  Estimation of laser hybrid welded joint strength by using genetic algorithm approach , 2008 .

[12]  G. Casalino.  Statistical analysis of MIG-laser CO2 hybrid welding of Al–Mg alloy , 2007 .

[13]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[14]  Lei Zhenglong,et al.  A comparative study of laser-arc double-sided welding and double-sided arc welding of 6 mm 5A06 aluminium alloy , 2011 .

[15]  Sudipto Chaki,et al.  Estimation and optimization of depth of penetration in hybrid CO2 LASER-MIG welding using ANN-optimization hybrid model , 2010 .

[16]  G. Casalino,et al.  An ANN and Taguchi algorithms integrated approach to the optimization of CO2 laser welding , 2006, Adv. Eng. Softw..

[17]  G. M. Eboo,et al.  Arc augmented laser welding , 1979 .

[18]  Song Gang,et al.  Hybrid laser–TIG welding, laser beam welding and gas tungsten arc welding of AZ31B magnesium alloy , 2004 .

[19]  Priti Wanjara,et al.  Hybrid fiber laser – Arc welding of thick section high strength low alloy steel , 2011 .

[20]  Luigi Maria Galantucci,et al.  A Quality Evaluation Method for Laser Welding of Al Alloys Through Neural Networks , 2000 .

[21]  Cui Li,et al.  Fiber laser-GMA hybrid welding of commercially pure titanium , 2009 .

[22]  Ming Gao,et al.  Effect of welding wires on microstructure and mechanical properties of 2A12 aluminum alloy in CO2 laser-MIG hybrid welding , 2009 .