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김준완(Jun W. Kim),김태원(T-W. Kim) 대한기계학회 2002 대한기계학회 춘추학술대회 Vol.2002 No.8
Densification occurs by the inelastic flow of the matrix materials of MMCs during the consolidation processes at high temperatures. The results depend on many process conditions such as applied pressure, temperature and volume fraction of fiber and matrix materials. This is particularly important in titanium matrix composites since materials failure may occur by either the applied conditions or microstructural factors through the processes. A generic model based on micro-mechanical approaches, therefore enabling the evolution of density over time to be predicted has been developed. Increasing temperature or pressure is shown to lead to increasing densification rate but the process-variables should be chosen by the consideration of the consequences of geometrical parameters for the materials. The mode developed is then implemented into finite element software so that practical process simulation has been carried out. Further the experimental investigation of the densification behavior of SiC/Ti-6Al-4V composites using vacuum hot pressing, together with thermo AE analysis has been performed, and the results obtained are compared with the model predictions.
김태원(T.W.Kim),서일홍(I.H.Suh),조영조(Y.J.Cho) 대한전자공학회 1992 대한전자공학회 학술대회 Vol.1992 No.10
It is shown that there exists a nonlinear mappping which transforms features and their changes to the desired camera motion without measurement of the relative distance between the camera and the part, and the nonlinear mapping can eliminate several difficulties encountered when using the inverse of the feature Jacobian as in the usual feature-based visual feedback controls. And insted of analytically deriving the closed form of such a nonlinear mapping, a fuzzy membership function (FMF) based neural network is then proposed to approximate the nonlinear mapping, where the sutructure of proposed networks is similar to that of radial basis function neural network which is known to be very useful in function approximations. The proposed FMF network is trained to be capable of tracking moving parts in the whole work space along the line of sight. For the effective implementation of proposed FMF networks, an image feature selection processing is investigated, and required fuzzy membership functions are designed. Finally, several numerical examples are illustrated to show the validities of our proposed visual servoing method.