Modelling and optimization of a GMA welding process by genetic algorithm and response surface methodology

The welding process, due to its complexity, has relied on empirical and experimental data to determine its welding conditions. However, trial-and-error methods to determine optimal conditions incur considerable time and cost. In order to overcome these problems, a genetic algorithm and response surface methodology have been suggested for determining optimal welding conditions. First, in a relatively broad region, near-optimal conditions were determined through a genetic algorithm. Then, the optimal conditions for welding were determined over a relatively small region around these near-optimal conditions by using response surface methodology. In order to give different objective function values according to the positive or negative response from the set target value in the optimization problem, a desirability function approach was used. Application of the method proposed in this paper revealed a good result for finding the optimal welding conditions in the gas metal arc (GMA) welding process.

[1]  Douglas C. Montgomery,et al.  Modified Desirability Functions for Multiple Response Optimization , 1996 .

[2]  Y. S. Tarng,et al.  The Use of Fuzzy Logic in the Taguchi Method for the Optimisation of the Submerged Arc Welding Process , 2000 .

[3]  A. H. Kuhne,et al.  AN EXPERT SYSTEM FOR ROBOTIC ARC WELDING , 1987 .

[4]  Colin R. Reeves,et al.  Using Genetic Algorithms with Small Populations , 1993, ICGA.

[5]  Shuichi Fukuda,et al.  Expert system for determining welding condition for a pressure vessel. , 1990 .

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

[7]  Madhan Shridhar Phadke,et al.  Quality Engineering Using Robust Design , 1989 .

[8]  G. Karsai,et al.  Artificial neural networks applied to arc welding process modeling and control , 1989, Conference Record of the IEEE Industry Applications Society Annual Meeting,.

[9]  G. Derringer,et al.  Simultaneous Optimization of Several Response Variables , 1980 .

[10]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[11]  M. J. Bibby,et al.  An analysis of curvilinear regression equations for modeling the submerged-arc welding process , 1993 .

[12]  Y. S. Tarng,et al.  A comparison between the back-propagation and counter-propagation networks in the modeling of the TIG welding process , 1998 .

[13]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  Douglas C. Montgomery,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[15]  Murray Smith,et al.  Neural Networks for Statistical Modeling , 1993 .

[16]  D. Kim,et al.  Optimization of arc welding process parameters using a genetic algorithm , 2001 .