Modeling and optimization of flux cored arc welding by genetic algorithm and simulated annealing algorithm

Purpose – The purpose of this study is to optimize the process parameters (wire feed rate (F), voltage (V), welding speed (S) and torch angle (A)) in order to obtain the optimum bead geometry (bead width (W), reinforcement (R) and depth of penetration (P)), considering the ranges of the process parameters using evolutionary algorithms, namely genetic algorithm (GA) and simulated annealing (SA) algorithm. Design/methodology/approach – The modeling of welding parameters in flux cored arc welding process using a set of experimental data and regression analysis, and optimization using GA and SA algorithm. Findings – The adequate mathematical model was developed. The multiple objectives were optimized satisfactorily by the GA and SA algorithms. The feasible solution results are very closer to the optimized results and the percentage error was found to be negligibly small. Originality/value – The optimal welding parameters were identified in order to increase the productivity. The welding input parameters effec...

[1]  Larry Jeffus Welding: Principles and Applications , 1984 .

[2]  Sehun Rhee,et al.  Modelling and optimization of a GMA welding process by genetic algorithm and response surface methodology , 2002 .

[3]  S. Natarajan,et al.  Optimization of GMAW process parameters in austenitic stainless steel cladding using genetic algorithm based computational models , 2013, Experimental Techniques.

[4]  H. R. Ghazvinloo,et al.  Influence of Wire Feeding Speed, Welding Speed and Preheating Temperature on Hardness and Microstructure of Weld in RQT 701-british Steel Produced by FCAW , 2010 .

[5]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[6]  M.S.J. Hashmi,et al.  Optimizing the laser-welded butt joints of medium carbon steel using RSM , 2005 .

[7]  N. Murugan,et al.  Effects of MIG process parameters on the geometry of the bead in the automatic surfacing of stainless steel , 1994 .

[8]  H. R. Ghazvinloo,et al.  Effect of Gas-Shielded Flux Cored Arc Welding Parameters on Weld Width and Tensile Properties of Weld Metal in a Low Carbon Steel , 2010 .

[9]  Amir Abdollah-zadeh,et al.  Effect of Welding Parameters on Dilution and Weld Bead Geometry in Cladding , 2009 .

[10]  Yu Xue,et al.  Fuzzy regression method for prediction and control the bead width in the robotic arc-welding process , 2005 .

[11]  D. Radaj Heat Effects of Welding: Temperature Field, Residual Stress, Distortion , 1992 .

[12]  N. Murugan,et al.  Prediction and optimization of weld bead volume for the submerged arc process - Part 1 , 2000 .

[13]  Elena Koleva,et al.  Statistical modelling and computer programs for optimisation of the electron beam welding of stainless steel , 2001 .

[14]  N. Murugan,et al.  Prediction and comparison of the area of the heat-affected zone for the bead-on-plate and bead-on-joint in submerged arc welding of pipes , 1999 .

[15]  Y. S Tarng,et al.  Modeling, optimization and classification of weld quality in tungsten inert gas welding , 1999 .

[16]  M. Hashmi,et al.  Effect of laser welding parameters on the heat input and weld-bead profile , 2005 .

[17]  Ill-Soo Kim,et al.  A study on relationship between process variables and bead penetration for robotic CO2 arc welding , 2003 .

[18]  Masatoshi Aritoshi,et al.  Metallurgical and Mechanical Properties of High Nitrogen Austenitic Stainless Steel Friction Welds , 2002 .

[19]  Young-Soo Yang,et al.  Sensitivity analysis for process parameters in GMA welding processes using a factorial design method , 2003 .

[20]  Dieter Radaj,et al.  Heat effects of welding , 1992 .

[21]  N. Murugan,et al.  Effect of submerged arc process variables on dilution and bead geometry in single wire surfacing , 1993 .

[22]  P. Thomson,et al.  FCAW process to avoid the use of post weld heat treatment , 2006 .

[23]  M. J. Bibby,et al.  Linear regression equations for modeling the submerged-arc welding process , 1993 .

[24]  Davi Sampaio Correia,et al.  Comparison between genetic algorithms and response surface methodology in GMAW welding optimization , 2005 .

[25]  V. Gunaraj,et al.  Application of response surface methodology for predicting weld bead quality in submerged arc welding of pipes , 1999 .

[26]  Y. S. Tarng,et al.  Process parameter selection for optimizing the weld pool geometry in the tungsten inert gas welding of stainless steel , 2002 .

[27]  T. Kannan,et al.  Effect of flux cored arc welding process parameters on duplex stainless steel clad quality , 2006 .

[28]  N. Murugan,et al.  Prediction and optimization of weld bead volume for the submerged arc process. Part 2 , 2000 .

[29]  A. G. Olabi,et al.  Optimization of different welding processes using statistical and numerical approaches - A reference guide , 2008, Adv. Eng. Softw..

[30]  V. K. Gupta,et al.  Fractional factorial technique to predict dimensions of the weld bead in automatic submerged arc welding , 1989 .

[31]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[32]  Prasad K. Yarlagadda,et al.  A study on prediction of bead height in robotic arc welding using a neural network , 2002 .