基于神经网络的修形斜齿轮成形磨削中的接触线优化方法  

Optimization of Contact Line for Form-grinding Modified Helical Gears Based on Neural Network

作　　者：汪中厚[1] 朱文敏[1] 李刚[1] 耿直[1] 

机构地区：[1]上海理工大学,上海200093

出　　处：《中国机械工程》2014年第12期1665-1671,共7页China Mechanical Engineering

基　　金：国家自然科学基金资助项目(51075279);上海市教委重点学科建设资助项目(J50503);上海市教委科研创新重点资助项目(10ZZ92)

摘　　要：基于接触线方程是一个超越方程,砂轮安装角和接触线形态之间的关系无法用明确的函数表示的情况,首先以接触线形态的三个评价参数(超程量、偏移量、偏置量)为目标函数,以砂轮安装角为变量,建立接触线优化模型。其次在评价参数的求解过程中,引入神经网络来求解,以砂轮安装角为神经网络的输入,接触线形态的评价参数为网络的输出,训练神经网络,结果表明训练后的神经网络不仅能做出正确的反应,而且具有其他方法所不具有的优点。最后以一种直线齿端齿向修形斜齿轮为例进行接触线优化,计算结果表明该方法可有效减小修形斜齿轮成形磨削中的磨削误差。磨齿实验验证了该方法的有效性。Since the contact line equation is a transcendental equation,the relationship between the installation angle and the shape could not be expressed by explicit functions,which made it difficult to obtain the optimal shape, this paper firstly took three evaluation parameters of the shape, overrun, shift and offset as the objective function as well as the installation angle as variables then the contact line optimization model was establish. Secondly,a neural network was introduced to solve the evaluation parameters. Through training the neural network by setting the installation angle as the input, the evaluation parameters as the output, the results show that the trained neural network can respond correctly,and has the advantages which the other methods do not obtain. As an example of end relief modified helical gear,the results show that the method can reduce the grinding errors effectively. Finally,the grinding experiments proven the effectiveness of the method.

关 键 词：成形磨削 接触线优化 神经网络 磨削误差 

分 类 号：TG61[金属学及工艺—金属切削加工及机床]