Radial thermal error modeling of CNC worm wheel gear grinding machine based on probabilistic neural network

被引：0

作者：

Zhong, Jintong ^{[1
]}

Li, Guolong ^{[1
]}

Liu, Dabin ^{[1
]}

Liu, Pengxiang ^{[1
]}

Liao, Lin ^{[1
]}

机构：

[1] State Key Lab of Mechanical Transmission, Chongqing University, Chongqing,400044, China

来源：

Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2017年 / 23卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Grinding (machining) - Error compensation - Worm gears - Wheels;

D O I：

10.13196/j.cims.2017.03.011

中图分类号：

学科分类号：

摘要：

The radial thermal error was the key factors of thermal error which directly affected M value of gear in CNC worm wheel gear grinding machine. Therefore, a method for the radial thermal error compensation of CNC worm wheel gear grinding machine based on Probabilistic Neural Network(PNN) was put forward. The temperature variables was optimized and selected by adopting the methods of fuzzy clustering, and the thermal error compensation model were set up by PNN. The compensation model was tested with CNC worm wheel gear grinding machine. The result showed that the error of the gears M Value was reduced from 0.06 mm to 0.01 mm or less. The above research indicated that the PNN model had high accuracy for thermal error modeling of CNC worm wheel gear grinding machine. Through comparing with other model, the stability and the feasibility of this model were verified. © 2017, Editorial Department of CIMS. All right reserved.

引用

页码：534 / 541