|제목(국문)||신경망과 유한요소법을 이용한 단조품의 초기 소재 형상 결정|
|제목(영문)||Determination of Initial Billet Size using The Artificial Neural Networks and The Finite Element Method for a Forged Product|
고대철 (D.C.Ko ,부산대하교 대학원 ) ▷공저자 네트워크 보기
김병민 (B.M.Kim ,부산대학교 정밀정형 및 금형가공 연구센터 ) ▷공저자네트워크등록하기
최재찬 (J.C.Choi ,부산대학교 정밀정형 및 금형가공 연구센터 ) ▷공저자네트워크등록하기
In the paper, we have proposed a new method to determine the initial billet for the forged products using a function approximation if the neural network. The architecture of neural network is a three-layer neural network and the back propagation algorithm is employed to train the network. By utilizing the ability of function approximation of a neural network, an optimal billet is determined by applying the nonlinear mathematical relationship between the aspect ratios in the initial billet and the final products. The amount of incomplete filling in the die is measured by the rigid-plastic finite element method. The neural network is trained with the initial billet aspect ratios and those of the unfilled volumes. After learning, the system is able to predict the filling regions which are exactly the same or slightly different to the results of finite element simulation. This new method is applied to find the optimal billet size for the plane strain rib-web product in cold forging. This would reduce the number of finite element simulation for determining the optimal billet size of forging product, further it is usefully adapted to physical modeling for the forging design.
|keyword||Metal Froming Process. Initial Billet, Artificial Neural Network, Function Approximation, Back Propagation Training Algorithm|