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신성우(S. W. Shin),박형종(H. J. Park),조진래(J. R. Cho) 대한기계학회 2002 대한기계학회 춘추학술대회 Vol.2002 No.8
For the high temperature engineering applications, materials used are required to possess superior thermo-mechanical performances, such as high temperature strength and creep resistance, especially fracture toughness and thermal shock resistance. So, laminated composites have been used for several decades as a heat-resisting material. But, there exists one inevitable disadvantage in classical laminate composites. To resolve this problem, a notion of functionally graded materials(FGM) was introduced. In FGMs, a material composition varies continuously from one end to the other of graded layer. In oder to find suitable volume fraction distribution in the graded layer for relaxing thermal stress, we apply optimization technique and neural network algorithm. The purpose of the use of neural network is to minimize the total CPU time by reducing the number of finite element analysis. As well, we confirmed that the neural network leads to reliable approximation of the objective function.
타이어 접지압 분포특성 규명을 위한 신경회로망 적용연구
신성우(S. W. Shin),정현성(H. S. Jeong),조진래(J. R. Cho),김남전(N. J. Kim),김기운(K. W. Kim) 대한기계학회 2002 대한기계학회 춘추학술대회 Vol.2002 No.4
Tire is a very important part of automobile because it supports entire automobile weight. As well, it characterizes major tire and automobile performances. Here, we intend to analyse contact pressure characteristics of tire by using neural algorithm. The tire analysis includes very difficult issues such as contact treatment, nonlinear material behavior and 3D analysis for contact pressure, and which requires considerably long CPU time. In order to minimize the numerical analysis cost, we employ a neural network for learning the tire contact pressure behavior.