Papers by Keyword: Ceramic Composite

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Authors: S.M. Zhang, Shi Pu Li, Q.S. Tang, Y.C. Han
433
Authors: Marc Poorteman, P. Descamps, F. Cambier, A. Poulet, Jean-Claude Descamps
173
Authors: Bao Feng Li, Jian Zheng, Xin Hua Ni, Yan Mei Qu, Xiao Wen Li, Shu Zeng Zhao
Abstract: The resistance pressure was the key to solve these problems that long rod projectiles penetrated ceramic targets at high velocity. Based on the twin shear united strength theory and the A-T model, the penetration depth were calculated. But the calculation result didn’t agree with experiment data. So the tension-compression ratio was redefined to apply to the dynamics problems according to the experiment data. And satisfied results were obtained.
353
Authors: Zhi Yuan Rui, Hong Yan Duan, Chun Li Lei, Xing Chun Wei
Abstract: Artificial neural network (ANN) back-propagation model was developed to predict the fracture design parameters in reinforced ceramic matrix composites (CMCS).Training ANN model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, the presetting deflection and tip radius of the notch, and the output, the cycle times of fracture were used during the network training. The neural network architecture is designed. The ANN model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. The ANN model exhibited excellent comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. It is possible to claim that, ANN is fairly promising prediction technique if properly used.
108
Authors: Hong Yan Duan, You Tang Li, Chun Li Lei, Gui Ping He
Abstract: Artificial neural network (ANN) back-propagation model was developed to predict the thermal expansion behavior and internal residual strains in reinforced ceramic matrix composites (CMCS).The ANN training model has been used to predict the thermal expansion behavior and internal residual strains, exhibiting excellent comparison with the experimental results. It was concluded that predicted thermal expansion behavior and internal residual strains by the trained neural network model seem more reasonable compared to approximate methods. It is possible to claim that, ANN is fairly promising prediction technique if properly used. Training ANN model was introduced at first. And then the neural network architecture is designed. The performance of system is summarized at last. In order to facilitate the comparisons of predicted values, the error evaluation and mean relative error are obtained. The result shows that the training model has good performance, and the experimental data and predicted data from ANN are in good coherence.
154
Authors: Naoki Miyano, H. Iwasa, Kazuo Isonishi, S. Tanaka, S. Sugiyama, Kei Ameyama
311
Authors: Gerd Willmann, W. von Chamier, H.-G. Pfaff, R. Rack
569
Authors: E. Fénard, Martine Desmaison-Brut, David J. Baxter
1165
Authors: Marco Antônio Schiavon, I.V.P. Yoshida, A.C. Silva, Wilson Acchar
Abstract: Ceramic matrix composites (CMC) were prepared by the active-filler-controlled polymer pyrolysis process (AFCOP) using a polysilsesquioxane resin filled with metallic niobium and alumina powders. Samples containing 60 wt% of polysilsesquioxane and 40 wt% of metallic niobium and alumina powders mixtures were homogenized, uniaxially pressed and pyrolysed in an alumina tube furnace up to 1400 °C, under argon flow. The ceramic products were characterized by X-ray diffraction (XRD), thermogravimetry (TGA), differential thermal analysis (DTA), Fourier transform infrared (FTIR) and energy-dispersive (EDS) spectroscopies. XRD analysis of the products showed the presence of crystalline phases such as NbC, Nb3Si, Nb5Si3, SiC, crystoballite and mullite. Thermogravimetry data of the composites presented low weight losses at 1000 °C. DTA curves showed an endothermic peak at 1350 °C, which was associated to the beginning of carbothermic reduction and/or the formation of silicon oxide and carbide. In addition, an exothermic peak at 1400 °C was associated to the formation of the mullite phase.
369
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