【摘要】 小圆角齿形双离合器盘毂在冲挤过程中,往往由于成形阻力大而致使小圆角齿形填充不完全。提出一种将坯料预制成内凹型的预成形方法,采用力学分析和有限元模拟对内凹型预制坯促进圆角填充进行分析,利用冲挤复合成形技术成形双离合器盘毂件,使用deform对预成形工件挤齿过程进行数值模拟,分析预制坯几何参数对成形质量的影响规律,利用BP神经网络建立预制坯几何参数和齿形圆角未填充宽度之间的映射关系,运用多目标遗传优化算法对预制坯几何参数进行了优化。结果表明,当坯料形状截面为内凹形的时候,因坯料与模具接触面积减少从而使摩擦阻力减小,同时对坯料产生切向推力,小圆角齿形填充较好;BP神经网络结合遗传算法能够可靠地优化预制坯几何参数,优化后圆角齿形填充性能得到了改善,提高了双离合器盘毂件成形质量。根据模拟结果进行试制,验证了工艺的可行性和模拟的准确性。更多还原

【Abstract】 In extruding the dual clutch hub with small fillet gear,the filling of small fillet gear is not complete due to the large forming resistance. A method of preforming the blank into a concave shape is proposed. The mechanical analysis and finite element simulation are used to analyze the principle that the blank of concave shape can promote the filling of small fillet gear,stamping and extruding are both used to form the dual clutch hub and the preform is used to simulate the fillet gear’s extruding to analyze the influence of the geometric parameters of the preform on the forming quality. The mapping relationship between the geometric parameters of the preform and the size of unfilled fillet gear is established by using the BP neural network. The multi-objective genetic algorithm is used to optimize the geometric parameters of the preform. From the results we can see that the frictional resistance decreases due to reduced contact area between the blank and the mold when the section shape of the blank is concave,and at the same time,the tangential thrust is generated on the blank,so the filling of small fillet gear is better; BP neural network combined with genetic algorithm can reliably optimize the geometric parameters of the preform. The filling performance of the fillet gear is better under the optimal preform,and the forming quality of the dual clutch hub is improved. The experimental result verified the feasibility of the method and the accuracy of the simulation.更多还原