机床主轴热关键点选择与典型转速热误差预测

doi:10.13229/j.cnki.jdxbgxb20210610

吉林大学学报(工学版) ›› 2023, Vol. 53 ›› Issue (1): 72-81.doi: 10.13229/j.cnki.jdxbgxb20210610

机床主轴热关键点选择与典型转速热误差预测

郭世杰^1,²(),张学炜^1,^2,³(),张楠^1,²,乔冠^1,²,唐术锋^1,²

^1.内蒙古工业大学机械工程学院，呼和浩特 010051
^2.内蒙古自治区特殊服役智能机器人重点实验室，呼和浩特 010051
^3.浙江大学机械工程学院，杭州 310063

收稿日期:2021-06-20 出版日期:2023-01-01 发布日期:2023-07-23
通讯作者: 张学炜 E-mail:sjguo@imut.edu.cn;zxw@imut.edu.cn
作者简介:郭世杰（1985-），男，副教授，博士. 研究方向：智能制造与装备. E-mail： sjguo@imut.edu.cn
基金资助:
国家自然科学基金项目(52065053);中央引导地方科技发展专项项目(2020ZY0002);内蒙古关键技术攻关项目(2020GG0255);内蒙古自然科学基金项目(2019BS05008);内蒙古自治区高等学校科学研究项目(NJZY21308);内蒙古自治区直属高校基本科研业务费项目(JY20220046);内蒙古自治区高等学校青年科技英才支持计划资助项目(NJYT23043)

Thermal key point select and error prediction under typical speed of machine tool spindle

Shi-jie GUO^1,²(),Xue-wei ZHANG^1,^2,³(),Nan ZHANG^1,²,Guan QIAO^1,²,Shu-feng TANG^1,²

^1.College of Mechanical Engineering，Inner Mongolia University of Technology，Hohhot 010051，China
^2.Inner Mongolia Key Laboratory of Special Service Intelligent Robotics，Inner Mongolia Autonomous Region，Hohhot 010051，China
^3.School of Mechanical Engineering，Zhejiang University，Hangzhou 310063，China

Received:2021-06-20 Online:2023-01-01 Published:2023-07-23
Contact: Xue-wei ZHANG E-mail:sjguo@imut.edu.cn;zxw@imut.edu.cn

摘要/Abstract

摘要：

针对机床主轴热误差对准静态精度影响的关键问题，提出了一种基于改进鸡群优化（MCSO）算法及支持向量（SVM）的热误差预测模型。利用基于非监督学习的谱聚类与Spearman关联分析辨识主轴关键敏感温度测点，降低温度数据分布于数量的依赖，削弱温度变量间的多重共线性。引入Levy飞行策略至母鸡个体局部搜索过程，构建了非线性动态自适应惯性权重更新雏鸡策略，基于MCSO-SVM进行核函数、罚因子以及偏差量的全局优化，分别采用MCSO-SVM、BP-GA、GA-SVM和CSO-SVM热误差建模，同时对不同转速下的模型预测能力进行对比分析。热误差实验测量与预测结果表明：谱聚类与Spearman关联分析可有效降低温度变量共线性导致的耦合作用；MCSO-SVM可实现典型转速下主轴五项热误差的高精度预测，模型具备较好的泛化能力和鲁棒性。

关键词: 机床, 热误差, 谱聚类, 改进鸡群算法, 支持向量机

Abstract:

Aiming at the key problem of the influence of thermal error on the static accuracy of machine tool spindle， a thermal error prediction model based on modified chicken swarm optimization algorithm （MCSO） and support vector machine （SVM） is proposed. Spectral clustering based on unsupervised learning and Spearman correlation analysis are used to identify the key sensitive temperature measuring points of the spindle， so as to reduce the number dependence of temperature data distribution and weaken the multicollinearity among temperature variables. Levy flight strategy is adopted in the local search of hens， and a nonlinear dynamic adaptive inertia weight for updating chick strategy is constructed to realize the global optimization of kernel function， and then MCSO is used to optimize the kernel function， penalty factor and deviation of SVM. MCSO-SVM， BP-GA， GA-SVM and CSO-SVM are used to establish thermal error models respectively. Meanwhile， the prediction ability of models under different working conditions is compared and analyzed. The experimental results of thermal error show that spectral clustering and Spearman correlation analysis can effectively reduce the coupling effect caused by collinearity of temperature variables； MCSO-SVM can achieve high-precision prediction of spindle thermal errors under typical speed， and the model has good generalization ability and robustness.

Key words: machine tool, thermal error, spectral clustering, modified chicken colony algorithm, support vector machine

中图分类号:

TH161

郭世杰,张学炜,张楠,乔冠,唐术锋. 机床主轴热关键点选择与典型转速热误差预测[J]. 吉林大学学报(工学版), 2023, 53(1): 72-81.

Shi-jie GUO,Xue-wei ZHANG,Nan ZHANG,Guan QIAO,Shu-feng TANG. Thermal key point select and error prediction under typical speed of machine tool spindle[J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(1): 72-81.

图/表 10

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图6

表1

图7

表2

热误差拟合优度对比"

模型	参数	指标
模型	参数	\|e_i \|_min	\|e_i \|_max	\|e_i \|	RMSE	R²	η
BP?GA	E $x$	7.7239	0.2584	3.05465	3.7329	0.9497	87.81
	θ_x	0.0133	0.0017	0.0077	0.0085	0.8390	82.59
	θ_y	0.0126	0.0027	0.0067	0.0073	0.9519	89.26
CSO?SVM	E $x$	5.8655	0.6151	1.844658	2.1517	0.9793	92.97
	θ_x	0.0106	0.0017	0.00541	0.0061	0.9130	89.35
	θ_y	0.0132	0.0015	0.005142	0.0060	0.9692	93.17
MCSO?SVM	E $x$	0.4898	2.9588	1.1878	1.3016	0.9907	94.33
	θ_x	0.0001	0.0075	0.0038	0.0043	0.9670	91.64
	θ_y	0.0002	0.0045	0.00257	0.0027	0.9932	94.91
GA?SVM	E $x$	1.5278	7.7445	4.0326	4.3426	0.8938	81.05
	θ_x	0.0004	0.0132	0.0053	0.0063	0.9310	90.27
	θ_y	0.0011	0.0121	0.0056	0.0062	0.9654	92.11

表2

图8

参考文献 26

1	Tan Feng, Yin Guo-fu, Zheng Kai, et al. Thermal error prediction of machine tool spindle using segment fusion LSSVM[J]. The International Journal of Advanced Manufacturing Technology, 2021,116(1/2):1-16.
2	李国龙,陶小会,徐凯,等. 数控机床转台位置相关几何误差的快速测量与辨识[J]. 吉林大学学报:工学版, 2021, 51(2): 458-467.
	Li Guo-long, Tao Xiao-hui, Xu Kai, et al. Rapid measurement and identification of position dependent geometric errors of CNC machine tool turntable[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 458-467.
3	Li Tie-jun, Sun Ting-ying, Zhang Yi-min, et al. Prediction of thermal error for feed system of machine tools based on random radial basis function neural network[J]. The International Journal of Advanced Manufacturing Technology, 2021, 114(5):1545-1553.
4	杜柳青,李仁杰,余永维. 基于注意力机制的时空卷积数控机床热误差模型研究[J]. 农业机械学报, 2021, 52(5):404-411.
	Du Liu-qing, Li Ren-jie, Yu Yong-wei. Spatiotemporal convolution thermal error model of CNC machine tools based on attention mechanism[J]. Transactions of the Chinese Society for Agricultural Machinery,2021, 52(5):404-411.
5	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 Yong-qing,et al. Review on thermal error compensation for feed axes of CNC machine tools[J]. Journal of Mechanical Engineering, 2021, 57(3):156-173.
6	Li Yang, Shi He-xuan, Ji Shi-jun, et al. Thermal positioning error modeling of servo axis based on empirical modeling method[J]. Micromachines, 2021, 12(2):1-22.
7	Fu Guo-qiang, Tao Chun, Xie Yun-peng, et al. Temperature-sensitive point selection for thermal error modeling of machine tool spindle by considering heat source regions[J]. The International Journal of Advanced Manufacturing Technology, 2021, 112(9/10):2447⁃2460 .
8	杜宏洋,陶涛,侯瑞生,等. 机床主轴轴向热误差一阶自回归建模方法[J].哈尔滨工业大学报,2021, 53(7):60-67.
	Du Hong-yang, Tao Tao, Hou Rui-sheng, et al. First-order autoregressive modeling method for axial thermal error of machine tool spindle[J]. Journal of Harbin Institute of Technology, 2021, 53(7):60-67.
9	赵亮,雷默涵,朱星星,等.高精度数控机床主轴系统热误差的控制方法[J].上海交通大学学报, 2020, 54(11):1165-1171.
	Zhao Liang, Lei Mo-han, Zhu Xing-xing, et al. A new thermal error control method for spindle system of high precision computer numerical control machine tools[J]. Journal of Shanghai Jiaotong University, 2020, 54(11):1165-1171.
10	谭峰,李成南,萧红,等.基于LSTM循环神经网络的数控机床热误差预测方法[J].仪器仪表学报,2020,41(9):79-87.
	Tan Feng, Li Cheng-nan, Xiao Hong, et al. A thermal error prediction method for CNC machine tool based on LSTM recurrent neural network[J]. Chinese Journal of Scientific Instrument, 2020, 41(9):79-87.
11	Huang Z, Liu Y, Du L, et al. Thermal error analysis, modeling and compensation of five-axis machine tools[J]. Journal of Mechanical Science and Technology, 2020, 34(10):4295-4305.
12	Yue Hai-tao, Guo Chen-guang, Li Qiang, et al. Thermal error modeling of CNC milling machine tool spindle system in load machining: based on optimal specific cutting energy[J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2020, 42(9):4561-4568
13	黄智,刘永超,邓涛,等. 一种五轴数控机床热误差建模方法[J]. 中国机械工程,2020,31(13):1529-1538.
	Huang Zhi, Liu Yong-chao, Deng Tao, et al. A method for thermal error modeling of FAMT[J]. China Mechanical Engineering, 2020, 31(13):1529-1538.
14	Shi Hu, Jiang Chun-ping, Yan Zong-zhuo, et al. Bayesian neural network-based thermal error modeling of feed drive system of CNC machine tool[J]. The International Journal of Advanced Manufacturing Technology, 2020, 108(9): 3031-3044.
15	马驰,杨军,梅雪松,等.基于遗传算法及BP网络的主轴热误差建模[J].计算机集成制造系统,2015,21(10):2627-2636.
	Ma Chi, Yang Jun, Mei Xue-song, et al. High-speed spindle thermal error modeling based on genetic algorithm and BP neural network[J]. Computer Integrated Manufacturing Systems, 2015, 21(10):2627-2636.
16	张蕾.基于GA-SVM的数控机床热误差建模研究[D]. 桂林:桂林电子科技大学材料科学与工程学院,2015.
	Zhang Lei. Research on thermal error modeling of NC machine tools based on GA-SVM[D]. Guilin: School of Materials Science and Engineering, Guilin University of Electronic Technology,2015.
17	马驰,赵亮,梅雪松,等. 基于粒子群算法与BP网络的机床主轴热误差建模[J]. 上海交通大学学报,2016, 50(5):686-695.
	Ma Chi, Zhao Liang, Mei Xue-song, et al. Thermal error modeling of machine tool spindle based on particle swarm optimization and neural network[J]. Journal of Shanghai Jiaotong University,2016, 50(5):686-695.
18	Meng X, Liu Y, Gao X, et al. A new bio-inspired algorithm: chicken swarm optimization[C]∥International Conference in Swarm Intelligence, Hefei,China,2014: 86-94.
19	Deb Sanchari, Gao Xiao-zhi, Tammi Kari, et al. Recent studies on chicken swarm optimization algorithm: a review (2014⁃2018)[J]. Artificial Intelligence Review, 2020, 53(4): 1737⁃1765.
20	罗勇,邵珠峰,王立平,等. NL201HA数控卧式车床X轴热误差建模及补偿[J]. 清华大学学报:自然科学版,2021,61(1):28-35.
	Luo Yong, Shao Zhu-feng, Wang Li-ping, et al. X-axis thermal error modeling and compensation for an NL201HA CNC horizontal lathe[J]. Journal of Tsinghua University(Science and Technology), 2021, 61(1):28-35.
21	Ma Chi, Liu Jia-lan, Wang Shi-long. Thermal error compensation of linear axis with fixed-fixed installation[J]. International Journal of Mechanical Sciences,2020,175:No.105531.
22	Groos Lisa, Held Christian, Keller Frank, et al. Mapping and compensation of geometric errors of a machine tool at different constant ambient temperatures[J]. Precision Engineering,2020, 63:10-17.
23	Yao Xiao-dong, Du Zheng-chun, Ge Guang-yan, et al. Dynamic temperature gradient and unfalsified control approach for machine tool thermal error compensation[J]. Journal of Mechanical Science and Technology, 2020,34(2):319-331.
24	Liu Yun-sheng, Miao En-ming, Liu Hui, et al. Robust machine tool thermal error compensation modelling based on temperature-sensitive interval segmentation modelling technology[J]. The International Journal of Advanced Manufacturing Technology,2020,106(1):655⁃669.
25	. Test code for machine tools-3: determination of thermal effects [S].
26	Yang J, Shi H, Feng B, et al. Thermal error modeling and compensation for a high-speed motorized spindle[J]. The International Journal of Advanced Manufacturing Technology, 2015, 77(5-8):1005-1017.

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测点	聚类组	相关系数	测点	聚类组	相关系数
T₁	1	0.9356	T₆	2	0.9519
T₂	4	0.9875	T₇	3	0.9547
T₃	4	0.9399	T₈	2	0.9717
T₄	4	0.8453	T₉	4	0.9356
T₅	5	0.9450	T₁₀	2	0.8865

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机床主轴热关键点选择与典型转速热误差预测

Thermal key point select and error prediction under typical speed of machine tool spindle

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