基于BP神经网络模型化Volterra非线性系统外圆磨削残余应力建模  

Modeling of Residual Stress in Cylindrical Grinding Base on Back Propagation Neural Networking Volterra Nonlinear System

作　　者：吕长飞[1,2] 吴小玉[1] 

机构地区：[1]贵州师范大学机械与电气工程学院,贵州贵阳550014 [2]贵州师范大学机械与控制仿真重点实验室,贵州贵阳550014

出　　处：《机床与液压》2014年第23期150-155,共6页Machine Tool & Hydraulics

基　　金：贵州师范大学博士启动基金资助项目(11904-05032130023);贵州联合科技基金资助项目(11904-0502213Y0112)

摘　　要：对各种不同磨削加工参数和磨削动态过程条件下,用BP神经网络化的Volterra级数建模,对外圆磨削加工时残余应力分布进行了建模仿真,实现了工件内部次表层残余应力大小与张应力峰值位置的预测,验证了多次磨削残余应力分布的非线性叠加关系,实现了外圆磨削次表层残余应力分布的计算。利用三层BP神经网络激励函数在阈值处进行泰勒级数分解,解算Volterra级数各阶核,采用离散Volterra网络学习算法,实现磨削动态系统残余应力非线性建模。并通过实验对模型进行了验证。A back propagation neural networking Volterra nonlinear system （ BPNNVNS） was used to modeling and simulate re-sidual stress distributions in cylindrical grinding under various grinding process parameters and grinding dynamics. Prediction of the magnitude of the residual stress and tensile peak location was implemented, the nonlinear superposition relationship in the residual stress distribution due to the number of grinding passes was verified, and the calculation of residual stress distribution in the subsurface of cylindrical grinding was achieved. The excitation function of BP neural network in three layers was decomposed with Taylor series on peaks to resolve all nucleus of Volterra series, the discrete Volterra network learning algorithm was used to achieve modeling of the non-linear residual stress in cylindrical grinding dynamic system. An experiment is used for the model development and validation.

关 键 词：外圆磨削 残余应力 VOLTERRA级数 BP神经网络 建模 

分 类 号：TG506[金属学及工艺—金属切削加工及机床]