On the basis of deep investigation of locomotive traction gears manufactured at home and abroad , a variety of measures are putted forward to improve the driving load-bearing capacity and working life of our country&#...On the basis of deep investigation of locomotive traction gears manufactured at home and abroad , a variety of measures are putted forward to improve the driving load-bearing capacity and working life of our country's high-speed locomotive traction gears. The measures include the fol- lowing five aspects : optimally selecting the material and heat treatment process , optimally designing the tooth profile . reasonably choosing the manufacture accuracy and technique , optimally choosing the lubricant and the way of lubrication and seal , improving the dynamic feature of the gearing. In the respect of the tooth profile , a hob with optimal cutter angles is designed to make root thickness on the dangerous section as large as possible and the stress concentration as small as possible. Ad- dendum modification coefficient is optimized to minimize the maximum flash temperature in the course of meshing. Finally . finite element analysis method is used to calculate the deformation and the stress of teeth accurately. And on this basis , optimal profile correction and axial modification have been designed with regard to the start , continuious running and high speed travel of the loco- motive .展开更多
In this paper, we present a new fruit fly optimization algorithm with the adaptive step for solving unconstrained optimization problems, which is able to avoid the slow convergence and the tendency to fall into local ...In this paper, we present a new fruit fly optimization algorithm with the adaptive step for solving unconstrained optimization problems, which is able to avoid the slow convergence and the tendency to fall into local optimum of the standard fruit fly optimization algorithm. By using the information of the iteration number and the maximum iteration number, the proposed algorithm uses the floor function to ensure that the fruit fly swarms adopt the large step search during the olfactory search stage which improves the search speed;in the visual search stage, the small step is used to effectively avoid local optimum. Finally, using commonly used benchmark testing functions, the proposed algorithm is compared with the standard fruit fly optimization algorithm with some fixed steps. The simulation experiment results show that the proposed algorithm can quickly approach the optimal solution in the olfactory search stage and accurately search in the visual search stage, demonstrating more effective performance.展开更多
文摘On the basis of deep investigation of locomotive traction gears manufactured at home and abroad , a variety of measures are putted forward to improve the driving load-bearing capacity and working life of our country's high-speed locomotive traction gears. The measures include the fol- lowing five aspects : optimally selecting the material and heat treatment process , optimally designing the tooth profile . reasonably choosing the manufacture accuracy and technique , optimally choosing the lubricant and the way of lubrication and seal , improving the dynamic feature of the gearing. In the respect of the tooth profile , a hob with optimal cutter angles is designed to make root thickness on the dangerous section as large as possible and the stress concentration as small as possible. Ad- dendum modification coefficient is optimized to minimize the maximum flash temperature in the course of meshing. Finally . finite element analysis method is used to calculate the deformation and the stress of teeth accurately. And on this basis , optimal profile correction and axial modification have been designed with regard to the start , continuious running and high speed travel of the loco- motive .
文摘In this paper, we present a new fruit fly optimization algorithm with the adaptive step for solving unconstrained optimization problems, which is able to avoid the slow convergence and the tendency to fall into local optimum of the standard fruit fly optimization algorithm. By using the information of the iteration number and the maximum iteration number, the proposed algorithm uses the floor function to ensure that the fruit fly swarms adopt the large step search during the olfactory search stage which improves the search speed;in the visual search stage, the small step is used to effectively avoid local optimum. Finally, using commonly used benchmark testing functions, the proposed algorithm is compared with the standard fruit fly optimization algorithm with some fixed steps. The simulation experiment results show that the proposed algorithm can quickly approach the optimal solution in the olfactory search stage and accurately search in the visual search stage, demonstrating more effective performance.
