Predicting Method of Rosidual Stress Using Artificial Neural Network In $CO_2$ Are Weldling

인공신경망을 이용한 탄산가스 아크용접의 잔류응력 예측

  • 조용준 (한양대학교 정밀기계공학과 대학원) ;
  • 이세현 (한양대학교 정밀기계공학과) ;
  • 엄기원 (한양대학교 정밀기계공학과)
  • Published : 1993.10.01

Abstract

A prediction method for determining the welding residual stress by artificial neural network is proposed. A three-dimensional transient thermomechanical analysis has been performed for the CO $_{2}$ Arc Welding using the finite element method. The validity of the above results is demonstrated by experimental elastic stress relief method which is called Holl Drilling Method. The first part of numarical analysis performs a three-dimensional transient heat transfer anslysis, and the second part then uses results of the first part and performs a three-dimensional transient thermo-clasto-plastic analysis to compute transient and residual stresses in the weld. Data from the finite element method were used to train a backpropagation neural network to predict residual stress. Architecturally, the finite element method were used to train a backpropagation voltage and the current, a hidden layer to accommodate failure mechanism mapping, and an output layer for residual stress. The trained network was then applied to the prediction of residual stress in the four specimens. The results of predicted residual stress have been very encouraging.

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