CNGC the 205 Institute

Laboratory, China

CNGC the 205 Institute

Laboratory, China
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Zhai Q.,Xi'an University of Science and Technology | Yang J.,Xi'an University of Science and Technology | Jing M.,CNGC the 205 Institute | Xu J.,Xi'an University of Science and Technology
Hanjie Xuebao/Transactions of the China Welding Institution | Year: 2010

The rapid solidification joining of quenched Cu-13.5%Sn /Ni-19.8%Sn heterogeneous alloy foils with the thickness of 40~60 μm was performed by a micro-type capacitor discharge welding machine and the microstructural morphology of joint was investigated as well as the effect of welding parameters on shearing strength of joint were researched. The results indicate that the capacitor discharge welding can realize the rapid solidification joining of quenched Cu-13.5%Sn/Ni-19.8%Sn dissimilar alloy foils. The joint consists of a nugget and fusion zone and the nugget is characterized by regular oblate spheroid with the major and minor diameters are 100 μm and 60 μm respectively. The nugget is mainly composed of blocky and stripy Ni-rich phase, fine and compact Cu13.7Sn columnar crystals and a-Cu phase which distributed with the a-Cu phase. The bonding zone width is about 2~3 μm. The welding parameters have obvious influence on the shearing strength of joint and the favorable welding parameters are C=3300 mf, F=12 N, U=75 V, D=0.18 mm, and the shearing strength of joint up to 590 MPa.


Niu W.,China Aeronautics 631 Institute | Niu W.,Northwestern Polytechnical University | Cheng J.,CNGC the 205 Institute | Wang G.,Shanghai Institute of Technology | Zhai Z.,Northwestern Polytechnical University
Journal of Computational and Theoretical Nanoscience | Year: 2013

The fault diagnosis and prediction of weapon equipments are getting more difficult for their complex structure, unique operating environment and multi-source faults. Currently although the main fault prediction methods have achieved certain success in practical application, they all fall short in some aspects. A multiple feature parameters prediction model is proposed by combing characteristics of rough sets and grey theory. Redundant information is eliminated from feature information by rough sets, and then the reduction information is quickly and accurately predicted by grey model. The simulation results show preliminarily that the model saves the computing time greatly and can effectively meet the requirements of fast and real-time fault prediction while precision is not reduced. Copyright © 2013 American Scientific Publishers All rights reserved.


Niu W.,Northwestern Polytechnical University | Wang G.,Chinese Aeronautical Radio Electronics Research Institute | Zhai Z.,Northwestern Polytechnical University | Cheng J.,CNGC the 205 institute
International Journal of Digital Content Technology and its Applications | Year: 2011

Fault prediction is of great importance to ensuring weapon equipment safety and reliability. Usually the data for fault detection and prediction of weapon equipment have feature like small samples, although the current main fault prediction methods have achieved certain success in practical application, they all fall short in some aspects. For chaos of weapon equipment fault data, based on rough sets and support vector machine modeling theory, an optimal least square support vector machine prediction method is proposed. Firstly, redundant information in time series is removed by rough sets. secondly, time series after reduction is prediction by support vector machine. The data of a certain aeroengine are taken as an example for prediction and analysis, and the results show that the model simplifies complexity of modeling and has high prediction precision, which in turn validates its availability.


Niu W.,Northwestern Polytechnical University | Wang G.,Chinese Aeronautical Radio Electronics Research Institute | Zhai Z.,Northwestern Polytechnical University | Cheng J.,CNGC the 205 institute
Advances in Information Sciences and Service Sciences | Year: 2011

Although the grey forecasting model has been successfully adopted in various fields and demonstrated promising results, the literatures show its performance could be further improved. The paper proves that the growth rate of the simulated value of the GM(1,1) is a fixed value. If the growth rates of the primary sequence are equate, the fitted value deriving from GM(1,1) is the same as the primary sequence, otherwise greater error would occur. In order to overcome shortcoming of the fixed growth rates, GM(1,1) is improved by introducing linear time-varying terms. Using the optimization method, the paper studies the iterative datum. The new model is called improved GM(1,1), abbreviated as IGM(1,1). Meanwhile, by contrasting IGM(1,1) model to the GM(1,1) model, the result shows that IGM(1,1) model has largely improves fitting and predicting precision.


Niu W.,Northwestern Polytechnical University | Cheng J.,CNGC the 205 Institute | Wang G.,Northwestern Polytechnical University | Wang G.,Chinese Aeronautical Radio Electronics Research Institute
Journal of Combinatorial Optimization | Year: 2013

Although the grey forecasting model has been successfully adopted in various fields and demonstrated promising results, the literatures show its performance could be further improved. For this purpose, this paper proves that the growth rate of the simulated value of the grey model GM(1,1) is a fixed value. If the growth rates of the primary sequence are equate, the fitted value deriving from GM(1,1) is the same as the primary sequence, otherwise greater error would occur. In order to overcome shortcoming of the fixed growth rates, extend the traditional GM(1,1) model by introducing linear time-varying terms, which can predict more accurately on non geometric sequences, termed EGM(1,1). Finally, compared with the other improved grey model and ARIMA model, experimental results indicate that the proposed model obviously can improve the prediction accuracy. © 2012 Springer Science+Business Media, LLC.

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