Probability distribution prediction of milling error generated by tool and artifact coupling deviation based on monte-carlo method

被引：1
作者：
School of Mechanical Engineering & Automation, Northeastern University, Shenyang [1 ]
110819, China
机构：
[1] School of Mechanical Engineering & Automation, Northeastern University, Shenyang
来源：
Dongbei Daxue Xuebao | / 7卷 / 985-990期
关键词：
Coupling deformation; Milling error; Monte-Carlo method; Neural network; Probability distribution;
D O I：
10.3969/j.issn.1005-3026.2015.07.016
中图分类号：
学科分类号：
摘要：
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. ©, 2015, Northeastern University. All right reserved.
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页码：985 / 990
页数：5
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