A Study on Bead Geometry Prediction the GMA Fillet Welding using Genetic Algorithm

유전자 알고리즘을 이용한 GMA 필릿 용접 비드형상 예측에 관한 연구

  • Kim, Young-Su (Dept. of Mechanical Engineering, Mokpo National University) ;
  • Kim, Ill-Soo (Dept. of Mechanical Engineering, Mokpo National University) ;
  • Lee, Ji-Hye (Dept. of Mechanical Engineering, Mokpo National University) ;
  • Jung, Sung-Myoung (Dept. of Mechanical Engineering, Mokpo National University) ;
  • Lee, Jong-Pyo (Dept. of Mechanical Engineering, Mokpo National University) ;
  • Park, Min-Ho (Dept. of Mechanical Engineering, Mokpo National University) ;
  • Chand, Reenal Ritesh (Dept. of Mechanical Engineering, Mokpo National University)
  • Received : 2012.12.12
  • Accepted : 2012.12.31
  • Published : 2012.12.31


The GMA welding process involves large number of interdependent variables which may affect product quality, productivity and cost effectiveness. The relationships between process parameters for a fillet joint and bead geometry are complex because a number of process parameters are involved. To make the automated GMA welding, a method that predicts bead geometry and accomplishes the desired mechanical properties of the weldment should be developed. The developed method should also cover a wide range of material thicknesses and be applicable for all welding position. For the automatic welding system, the data must be available in the form of mathematical equations. In this study a new intelligent model with genetic algorithm has been proposed to investigate interrelationships between welding parameters and bead geometry for the automated GMA welding process. Through the developed model, the correlation between process parameters and bead geometry obtained from the actual experimental results, predicts that data did not show much of a difference, which means that it is quite suitable for the developed genetic algorithm. Progress to be able to control the process parameters in order to obtain the desired bead shape, as well as the systematic study of the genetic algorithm was developed on the basis of the data obtained through the experiments in this study can be applied. In addition, the developed genetic algorithm has the ability to predict the bead shape of the experimental results with satisfactory accuracy.



Supported by : 교육과학기술부, 한국연구재단


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