基于蒙特卡洛法的铣削让刀误差概率分布预测  

Probability Distribution Prediction of Milling Error Generated by Tool and Artifact Coupling Deviation Based on Monte-Carlo Method

作　　者：张义民[1] 曹辉[1] 黄贤振[1] 

机构地区：[1]东北大学机械工程与自动化学院,辽宁沈阳110819

出　　处：《东北大学学报（自然科学版）》2015年第7期985-990,共6页Journal of Northeastern University(Natural Science)

基　　金：国家自然科学基金资助项目(51135003;51105062);国家重点基础研究发展计划项目(2014CB046303);"高档数控机床与基础制造装备"科技重大专项(2013ZX04011-011);沈阳市科技计划项目(F12-082-2-00)

摘　　要：基于斜角切削理论,建立铣削力计算模型,求解得到铣削力.构建薄板受力变形的挠度函数,结合刀具的受力变形求解刀具-工件耦合变形的铣削让刀误差.采用神经网络拟合方法,求出输入铣削参数与输出最大让刀误差的函数关系.考虑刀具参数、材料参数、工件参数以及加工工况等随机参数对金属切削的影响,利用蒙特卡洛方法,对输入参数进行抽样,将参数样本代入神经网络拟合的函数模型中,获得铣削让刀误差样本,并分析其概率特性,从而提出一种铣削让刀误差的概率分布预测方法,较确定性计算铣削让刀误差的方法更加符合实际.The milling force calculation model was established based on the theory of bevel cutting,and the milling force was obtained. The bend function of thin plate deformation was built,and the milling error in milling process of tool-workpiece coupling deformation was obtained based on the combination of tool deformation. Neural network fitting method was adopted to obtain the function relationship between the input milling parameters and the output maximum milling error. Considering the influence on metal cutting by the parameters of tool,material,workpiece and working condition,the input parameters were sampled by the Monte-Carlo method.The parameter samples were substituted into the function model which was fitted by neural network,and the milling error samples were obtained. Then a probability distribution prediction method of milling error was put forward by analyzing the probability characteristics of the milling error. It was closer to actual than the deterministic calculation of milling error.

关 键 词：铣削让刀误差 耦合变形 神经网络 蒙特卡洛法 概率分布 

分 类 号：TH161.12[机械工程—机械制造及自动化]