Thermal Error Modeling of Numerical Control Machine Tools Based on Neural Network Neural Network by Optimized SSO Algorithm

被引：3
|
作者：
Huang Z. [1 ]
Liu Y.-C. [1 ]
Liao R.-J. [2 ]
Cao X.-J. [2 ]
机构：
[1] School of Mechanical and Electrical Engineering, University of Electronic Science and Technology, Chengdu
[2] Sichuan Chengfei Integration Technology Corporation, Chengdu
来源：
Dongbei Daxue Xuebao/Journal of Northeastern University | 2021年 / 42卷 / 11期
关键词：
Five-axis NC(numerical control) machine tool; Shark smell optimization(SSO)algorithm; Temperature key point; Thermal error compensation; Thermal error modeling;
D O I：
10.12068/j.issn.1005-3026.2021.11.008
中图分类号：
学科分类号：
摘要：
In order to explore the complex thermal characteristics of five-axis NC(numerical control) machine tools, a method for thermal error modeling of cradle five-axis NC machine tools was proposed. The principle of shark smell optimization(SSO)algorithm and neural network composite modeling was adopted, which effectively improved the accuracy and modeling efficiency of the machine tool thermal error prediction model. Firstly, the temperature sensitive point was screened by using the thermal imager, and then the temperature sensor was placed at the position of the heat sensitive point of the machine tool. The collected thermal characteristic data were modeled by the above method. The results showed that the method is better than ABC neural network and PSO neural network in terms of modeling speed and accuracy. Finally, the model was applied to the thermal error compensation experiment of the five-axis machine tool, which improves its accuracy by 32%. © 2021, Editorial Department of Journal of Northeastern University. All right reserved.
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页码：1569 / 1578
页数：9
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