采用天鹰优化卷积神经网络的精密数控机床主轴热误差建模

李国龙; 陈孝勇; 李喆裕; 徐凯; 唐晓东; 王志远

doi:10.7652/xjtuxb202208006

您当前的位置：

首页 >

文章列表页 >

采用天鹰优化卷积神经网络的精密数控机床主轴热误差建模

—————————————————————— | 更新时间：2025-07-09

- 采用天鹰优化卷积神经网络的精密数控机床主轴热误差建模
- Thermal Error Modeling of Spindle for Precision CNC Machine Tool Based on AO-CNN
- 西安交通大学学报 2022年56卷第8期页码：51-61
- 作者机构：
  
  重庆大学机械传动国家重点实验室,重庆,400044
- 作者简介：
  
  : 2022-01-17。作者简介: 李国龙(1968—),男,教授,博士生导师。基金项目: 重庆市技术创新与应用发展专项重点资助项目(cstc2019jscx-mbdxX0034)
- 基金信息：
  
  : 2022-01-17。作者简介: 李国龙(1968—),男,教授,博士生导师。基金项目: 重庆市技术创新与应用发展专项重点资助项目(cstc2019jscx-mbdxX0034)
- DOI：10.7652/xjtuxb202208006
  中图分类号： TH161
- 网络首发：2022-08-10，
  
  纸质出版：2022
- 稿件说明：
移动端阅览
李国龙, 陈孝勇, 李喆裕, 等. 采用天鹰优化卷积神经网络的精密数控机床主轴热误差建模[J]. 西安交通大学学报, 2022,56(8):51-61.

LI Guolong, CHEN Xiaoyong, LI Zheyu, et al. Thermal Error Modeling of Spindle for Precision CNC Machine Tool Based on AO-CNN[J]. 2022, 56(8): 51-61.
李国龙, 陈孝勇, 李喆裕, 等. 采用天鹰优化卷积神经网络的精密数控机床主轴热误差建模[J]. 西安交通大学学报, 2022,56(8):51-61. DOI： 10.7652/xjtuxb202208006.

LI Guolong, CHEN Xiaoyong, LI Zheyu, et al. Thermal Error Modeling of Spindle for Precision CNC Machine Tool Based on AO-CNN[J]. 2022, 56(8): 51-61. DOI： 10.7652/xjtuxb202208006.

摘要

针对数控机床因主轴热误差而严重影响加工精度等问题

结合求解最优解能力强的天鹰优化算法(AO)以及自学习和自适应能力强的卷积神经网络(CNN)

提出一种采用AO-CNN的数控机床主轴热误差模型。根据磨齿加工过程特点

总结磨齿机主轴系统热变形规律

确定了X方向热误差为影响齿轮加工的主要因素; 利用模糊C均值聚类(FCM)和相关系数法筛选出关键温度点; 利用AO算法优化CNN结构的卷积核

并且建立AO-CNN的数控机床主轴X方向热误差预测模型。在2种不同转速的工况下对所建立模型的性能进行了验证

结果表明

采用AO-CNN进行热误差建模

数控机床X方向的热变形预测精度相比于CNN模型提高了15%

具有更加优越的预测精度。

Abstract

To accurately predict the thermal error of the spindle of a CNC machine tool and avoid serious impacts of such thermal error on gear machining accuracy

a thermal error model of grinding machine spindle based on AO-CNN is proposed by combining the convolutional neural network(CNN)with strong self-learning and self-adaptive abilities and the aquila optimizer(AO)with strong ability to solve the optimal solution. Firstly

the thermal deformation principle of the spindle and the grinding process were analyzed

and it is found that the thermal error in X direction is the main factor affecting the machining accuracy. Then

the key temperature points were selected by use of the fuzzy C-means clustering(FCM)algorithm with relevant coefficients. The convolution kernel of CNN structure was optimized by AO algorithm and the X-direction thermal error prediction model was established for the spindle of the CNC machine tool based on AO-CNN. Finally

the performance of the model was verified under two experimental conditions at different speeds. And the results show that the thermal error prediction accuracy of AO-CNN model in X direction of the CNC machine tool improved by 15% compared with the CNN model providing superior prediction accuracy.

关键词

Keywords

references

刘阔, 韩伟, 王永青, 等. 数控机床进给轴热误差补偿技术研究综述 [J]. 机械工程学报, 2021, 57(3): 156-173.

LIU Kuo, HAN Wei, WANG Yongqing, et al. Review on thermal error compensation for feed axes of CNC machine tools [J]. Journal of Mechanical Engineering, 2021, 57(3): 156-173.

栗世豪, 张俊, 唐宇阳, 等. 零件加工误差与机床几何误差映射关系建模 [J]. 西安交通大学学报, 2021, 55(10): 50-59.

LI Shihao, ZHANG Jun, TANG Yuyang, et al. Modeling of mapping relationship between machining error and geometric error of machine tool [J]. Journal of Xi'an Jiaotong University, 2021, 55(10): 50-59.

吕盾, 罗世有, 王大伟, 等. 考虑几何误差和跟随误差的五轴联动轨迹误差预测方法 [J]. 西安交通大学学报, 2021, 55(10): 38-49.

LÜ Dun, LUO Shiyou, WANG Dawei, et al. A trajectory error prediction method considering geometric errors and tracking errors for five-axis machine tools [J]. Journal of Xi'an Jiaotong University, 2021, 55(10): 38-49.

MAYR J, JEDRZEJEWSKI J, UHLMANN E, et al. Thermal issues in machine tools [J]. CIRP Annals, 2012, 61(2): 771-791.

RAMESH R, MANNAN M A, POO A N. Error compensation in machine tools: a review: part II thermal errors [J]. International Journal of Machine Tools and Manufacture, 2000, 40(9): 1257-1284.

王海同, 李铁民, 王立平, 等. 机床热误差建模研究综述 [J]. 机械工程学报, 2015, 51(9): 119-128.

WANG Haitong, LI Tiemin, WANG Liping, et al. Review on thermal error modeling of machine tools [J]. Journal of Mechanical Engineering, 2015, 51(9): 119-128.

苗恩铭, 高增汉, 党连春, 等. 数控机床热误差特性分析 [J]. 中国机械工程, 2015, 26(8): 1078-1084.

MIAO Enming, GAO Zenghan, DANG Lianchun, et al. Thermal error characteristics analysis of CNC machine tools [J]. China Mechanical Engineering, 2015, 26(8): 1078-1084.

LI Yang, ZHAO Wanhua, LAN Shuhuai, et al. A review on spindle thermal error compensation in machine tools [J]. International Journal of Machine Tools and Manufacture, 2015, 95: 20-38.

朱星星, 赵亮, 雷默涵, 等. 精密进给系统热误差的协同训练支持向量机回归建模与补偿方法 [J]. 西安交通大学学报, 2019, 53(10): 40-47.

ZHU Xingxing, ZHAO Liang, LEI Mohan, et al. Co-training support vector machine regression modeling and compensation for thermal error of precision feed system [J]. Journal of Xi'an Jiaotong University, 2019, 53(10): 40-47.

孙志超, 陶涛, 黄晓勇, 等. 车床主轴与进给轴耦合热误差建模及补偿研究 [J]. 西安交通大学学报, 2015, 49(7): 105-112.

SUN Zhichao, TAO Tao, HUANG Xiaoyong, et al. Modeling and compensation of coupled thermal error of spindle and feed shafts [J]. Journal of Xi'an Jiaotong University, 2015, 49(7): 105-112.

HUANG Yanqun, ZHANG Jie, LI Xu, et al. Thermal error modeling by integrating GA and BP algorithms for the high-speed spindle [J]. The International Journal of Advanced Manufacturing Technology, 2014, 71(9): 1669-1675.

YANG Jun, SHI Hu, FENG Bin, et al. Thermal error modeling and compensation for a high-speed motorized spindle [J]. The International Journal of Advanced Manufacturing Technology, 2015, 77(5): 1005-1017.

魏新园, 钱牧云, 冯旭刚, 等. 基于偏最小二乘的数控机床热误差稳健建模算法 [J]. 仪器仪表学报, 2021, 42(5): 34-41.

WEI Xinyuan, QIAN Muyun, FENG Xugang, et al. Robust modeling method for thermal error of CNC machine tools based on partial least squares algorithm [J]. Chinese Journal of Scientific Instrument, 2021, 42(5): 34-41.

YANG Bo, LIU Zihui. Thermal error modeling by integrating GWO and ANFIS algorithms for the gear hobbing machine [J]. The International Journal of Advanced Manufacturing Technology, 2020, 109(9): 2441-2456.

LI Zheyu, LI Guolong, XU Kai, et al. Temperature-sensitive point selection and thermal error modeling of spindle based on synthetical temperature information [J]. The International Journal of Advanced Manufacturing Technology, 2021, 113(3): 1029-1043.

张毅, 杨建国. 基于灰色理论预处理的神经网络机床热误差建模 [J]. 机械工程学报, 2011, 47(7): 134-139.

ZHANG Yi, YANG Jianguo. Modeling for machine tool thermal error based on grey model preprocessing neural network [J]. Journal of Mechanical Engineering, 2011, 47(7): 134-139.

CAO Huajun, ZHU Libin, LI Xianguang, et al. Thermal error compensation of dry hobbing machine tool considering workpiece thermal deformation [J]. The International Journal of Advanced Manufacturing Technology, 2016: 86(5): 1739-1751.

杨勇明, 汪中厚, 刘欣荣, 等. 磨齿机在机检测机构几何误差链建模与补偿 [J]. 仪器仪表学报, 2021, 42(6): 9-19.

YANG Yongming, WANG Zhonghou, LIU Xinrong, et al. Modeling and compensation of geometric error chain of on-machine inspection mechanism for gear grinder [J]. Chinese Journal of Scientific Instrument, 2021, 42(6): 9-19.

戴野, 尹相茗, 魏文强, 等. 基于ANFIS的高速电主轴热误差建模研究 [J]. 仪器仪表学报, 2020, 41(6): 50-58.

DAI Ye, YIN Xiangming, WEI Wenqiang, et al. Thermal error modeling of high-speed motorized spindle based on ANFIS [J]. Chinese Journal of Scientific Instrument, 2020, 41(6): 50-58.

LIU Zihui, YANG Bo, MA Chi, et al. Thermal error modeling of gear hobbing machine based on IGWO-GRNN [J]. The International Journal of Advanced Manufacturing Technology, 2020, 106(11): 5001-5016.

CALISKI T, HARABASZ J. A dendrite method for cluster analysis [J]. Communications in Statistics, 1974, 3(1): 1-27.

MILLIGAN G W, COOPER M C. An examination of procedures for determining the number of clusters in a data set [J]. Psychometrika, 1985, 50(2): 159-179.

高新波. 模糊聚类分析及其应用 [M]. 西安: 西安电子科技大学出版社, 2004.

ABUALIGAH L, YOUSRI D, ABD ELAZIZ M, et al. Aquila optimizer: a novel meta-heuristic optimization algorithm [J]. Computers Industrial Engineering, 2021, 157: 107250.

WEN Long, LI Xinyu, GAO Liang, et al. A new convolutional neural network-based data-driven fault diagnosis method [J]. IEEE Transactions on Industrial Electronics, 2018, 65(7): 5990-5998.

SCHMIDHUBER J. Deep learning in neural networks: an overview [J]. Neural Networks, 2015, 61: 85-117.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

蜉蝣优化双通道网络在齿轮箱故障诊断中的应用

一种适用于混合三端直流输电线路的故障定位方法

采用格拉姆角场-卷积神经网络-时序卷积网络混合模型的锂离子电池健康状态估计

局部放电脉冲群的实用快速分类技术及应用

车床主轴与进给轴耦合热误差建模及补偿研究