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According to the metal powder injection molding process, the main influence factors of injection molding billet density distribution (such as: injection velocity, injection temperature, injection pressure, etc) was analyzed and a multiple input & multiple output BP neural network model for injection molding was build up to predict the density distribution of the billet intelligently based on ANN and GA. In addition, in light of the requirements for the density distribution of the metal injection molding billet, the influence factors were controlled intelligently. Applying this model in the metal injecting process, the density distribution of billet was predicted according to the injection parameters and the injection parameters was optimized according to the required density distribution of the billet. As the result, the error was less than 5% between the prediction values and the actual values of the density distribution of billet. With the optimized injection parameters to the injection process, the density distribution of billet closed to the requirements was achieved.

Metal powders behave high strain rate, viscous effect and first hardening then softening deformation characteristics during the forming process of high velocity compaction. The characteristics of high strain rate and viscous effect are described by composite nonlinear viscoelastic body which consists of non-linear spring, linear spring and high strain rate Maxwell element. The deformation characteristics of first hardening then softening can be described by changing the degree of the term of nonlinear spring from greater than 1 to less than 1. Constitutive relation of metal powder in high velocity compaction is established. The degree of the term of nonlinear spring is considered as a function of strain. The function is approximated by linear, quadratic and cubic polynomial and the stress-strain curves are analyzed respectively. Analysis results indicate that the constitutive equation can describe the deformation characteristics of metal powder in high velocity compaction.

The peculiar character of flexible beam control and advanced waveform design of Active Electronically Steered Array (AESA) system brings powerful ECCM (Electronic Counter-Counter Measures) ability for airborne fire-control radar. This paper introduced all kinds of ECCM of airborne AESA radar first, especially for Low Probability of Intercept (LPI) technique and Space-Time Adaptive Processing (STAP) technique, then, it explored self-defense jamming approaches of combat aircraft in modern aerial warfare.