Multi-Objective Optimization Design And Performance Analysis Of The Aviatic Micro Beveloid Gear Reducer

Aeronautical and space technologies actively get rid of the stale and bring forth the fresh and design a perfect micro device. The qualification laid down strict, almost harsh, requirements for the micro reducer- the developing tendency of reducer is toward the orientation of miniaturization, large torque, high speed, stable drive, low noise and high reliability. For the special requirement of the adjustment device of the tail lamina of the rocket the paper develops the micro reducer. A series of theoretical research work is presented in follow:First, fix on adopting RV reducer after the entirely analysis of many kinds of device system. On the base of the study on some kinds of RV reducers, the Arc bevel gear transmission (high-speed level)and The adjustable gap thicken gear reducer device of variable thick tooth gear drive (low-speed level) are chosen. Conventional design methods are employed to determine primary structure parameters of reducer. Finite element theory and ANSYS software are adopted to force analysis and mode analysis of reducer key components and exploit the parametric design software for the Less thickening gear tooth difference, figure out the thickening gear problem of the complexity of the formula and the large quantity caused by the difference of coefficient, the vary of tooth profile factor.For the reason that multi-objective optimization in traditional need to deal with the aim function by Linear-weighted, could only achieve optimize approximately. The paper put forward a kind of accurate multi-objective optimization—double populations differential multi-storey culture particle swarm fusion arithmetic after improving. In the evolution process of belief space, the algorithm uses the strategy"Multi-space, Merit-based selection", avoid the shortcoming of local extremes which is caused by the dispatch of the belief space after updating. In the evolution process of the colony space. The algorithm uses the method of double populations differential multi-storey, avoid the shortcoming of the badly result caused by the discard of abundant high fitness feasible solution, improve the diversity of the colony and the convergence speed. It modify multi-objective optimization of culture particle swarm.For the reason that the aeronautical micro reducer exists the shortcoming involving the excessive parameters, constraints multiply, the complexity calculation and the fussy design, the paper uses the double populations differential multi-storey culture particle swarm fusion arithmetic to design in three objective in the aeronautical micro reducer, achieve the best solution, comparing with the tradition solution, optimize the problem.Using the spiral bevel gear as the research objective, the system put up the dynamic analysis of spiral bevel gear in the high level. After the integration consideration of the backlash, time-varying mesh stiffness, transmission error and other kinds of non-linear factors, establish the non-linear dynamic model of Spiral bevel gear pairs. Aim at the point of meshing stiffness, the periodicity of stiffness incentives in the transmission process, In this physical model, the time-varying stiffness has been expressed by a harmonic form with 5 orders and the nonlinear backlash function has been fitted by a polynomial of degree 7. The complicated nonlinear differential equations in dimensionless form has been presented in this dissertation. Using Gear method the reunification of the numerical simulation analysis in non-dimensional differential equations. The results of the various state are single-cycle harmonic, multi-cycle times harmonic, quasi-periodic and chaotic response, after the combination of history, floor plans, Poincare map and FFT spectrum map, the paper analysis and compares various types of detail.For the reason that the traditional reliability is very difficult, the process is fussy, the paper combines the dynamic process neural networks and Monte-Carlo methods which apply in the reliability analyses in the reducer device. The paper bring forward using the PSO to replace the traditional BP algorithm and apply in the process of the whole connection neural networks, train its connection weights, delete redundant link and make it part of the process of connecting neural network, optimize the structure of the network and increase the speed of network convergence and accuracy. On the basis of the deceleration device fault tree analysis, combine the ICDPNN and Monte-Carlo methods, make reliability study on adjustable air space thickness RV gear deceleration device. The results show that the deceleration device has high reliability, full compliant with the design requirements.Finally, all structure parameters of reducer sample machine are determined and the drawings of reducer are completed. The load capacity, transmission efficiency and vibration features of the prototype are investigated. The test of the sample machine indicates that it meets the demands of design completely.