Papers by Author: He Geng Wei

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Authors: Ning Ou Yang, Ying Liang, Chun Yue Huang, He Geng Wei
Abstract: Two communication cabinet finite element analysis(FEA) models with different cross-sectional structure vertical columns were set up. Based on the two communication cabinet FEA models, modal analysis was carried out by using the subspace method; the first 6 order natural frequencies and vibration modes were obtained. Harmonic response analysis was also carried out; the displacement response of the communication cabinet structure under external loading was determined. The dynamic performance comparison of the two communication cabinets with different cross-sectional structure vertical columns was performed, as a result, an effective method is provided for communication cabinet dynamic characteristic optimized design.
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Authors: Chun Yue Huang, He Geng Wei, Tian Ming Li, De Jin Yan
Abstract: By determining membership function of the input parameters and selecting defuzzification method, the evaluation model which can be used to intelligent analyzing the causes of SMT solder joint defects was set up. The fuzzy neural network was trained by using the output variables of the training samples from intelligent discrimination as the input variables of training samples of fuzzy neural network. The fuzzy neural network was tested by using the output variables of the testing samples from intelligent discrimination as the input variables of testing samples of fuzzy neural network. The results show that by using the evaluation model the cause of SMT solder joint defects can be analyzed intelligently and the results of intelligently analysis are reasonable, the evaluation model can be used practically.
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Authors: Chun Yue Huang, Tian Ming Li, Ying Liang, He Geng Wei
Abstract: In the thermal design of embedded multi-chip module (MCM), the placement of chips has a significant effect on temperature field distributing, thus influences the reliability of the embedded MCM. The thermal placement optimization of chips in embedded MCM was studied in this paper, the goal of this work is to decrease temperature and achieve uniform thermal field distribution within embedded MCM. By using ANSYS the finite element analysis model of embedded MCM was developed, the temperature field distributing was calculated. Based on genetic algorithms, chips placement optimization algorithm of embedded MCM was proposed and the optimization chips placement of embedded MCM was achieved by corresponding optimization program. To demonstrate the effectiveness of the obtained optimization chips placement, finite element analysis (FEA) was carried out to assess the thermal field distribution of the optimization chips placement in embedded MCM by using ANSYS. The result shows that without chips placement optimizing the maximum temperature and temperature difference in embedded MCM model are 87.963°C and 2.189°C respectively, by using chips placement optimization algorithm the maximum temperature drop than the original 0.583°C and the temperature difference is only 0.694°C . It turns out that the chip placement optimization approach proposed in this work can be effective in providing thermal optimal design of chip placement in embedded MCM.
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