数控车床主轴热误差完全自适应经验模态分解与小波阈值变换分离方法  

Separation Method of Completely Adaptive Empirical Mode Decomposition and Wavelet Threshold Transform for Spindle Thermal Error of CNC Machine Tool

作　　者：陈庚 丁强强 苏哲 郭世杰[1,3] 唐术锋[1,3] CHEN Geng;DING Qiangqiang;SU Zhe;GUO Shijie;TANG Shufeng(School of Mechanical Engineering,Inner Mongolia University of Technology,Hohhot 010051,China;Tarim University,Alaer 843300,China;Inner Mongolia Key Laboratory of Robotics and Intelligent Equipment Technology,Inner Mongolia University of Technology,Hohhot 010051,China)

机构地区：[1]内蒙古工业大学机械工程学院,呼和浩特010051 [2]塔里木大学,阿拉尔843300 [3]内蒙古工业大学内蒙古自治区机器人与智能装备技术重点实验室,呼和浩特010051

出　　处：《航空制造技术》2025年第6期104-114,共11页Aeronautical Manufacturing Technology

基　　金：国家自然基金(52065053,52365064);中央引导地方科技发展计划(2020ZY0002);内蒙古自然科学基金(20232LHMS05018);内蒙古自然科学基金(2020BS05003);内蒙古关键技术攻关计划(2021GG0255);内蒙古自治区高等学校创新团队发展计划支持(NMGIRT2213);内蒙古自治区直属高校基本科研业务费项目(ZTY2023005,JY20230043);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);内蒙古自治区“英才兴蒙”工程团队项目(2025TEL02)。

摘　　要：数控车床主轴热误差是影响车床加工精度的主要因素之一。为提高热误差测量准确度,降低测量技术要求,提出一种基于完全自适应噪声集合经验模态分解(ICEEMDAN)和经验小波变换(EWT)的车床热误差信息分离方法。首先,使用ICEEMDAN算法对原始信号进行分解,将获得的低频模态分量重构后作为EWT算法的输入进行分解,使用离散系数评估EWT算法每次迭代的分解效果。其次,通过对一组仿真信号进行分解,验证该方法的准确性,与ICEEMDAN算法相比,ICEEMDAN-EWT算法的均方根误差(RMSE)降低了5.2%。最后,在CKA6 163A型车床上进行试验,使用五点法辨识热误差,将ICEEMDAN-EWT分离算法与傅里叶变换(FFT)算法进行对比。结果表明,与FFT算法相比,使用ICEEMDAN-EWT算法分离出的5项热变形信号与机床温度的Pearson相关性提高了3.8%,Spearman相关性提高了6.6%,准确度更高。Thermal error of spindle of the CNC machine tool is one of the main factors affecting machining accuracy of the machine tool.In order to improve accuracy of the thermal error measurement and reduce the measurement technology requirements,a thermal error information separation method of machine tool based on the improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)and empirical wavelet transform(EWT)is proposed.Firstly,the original signal is decomposed using the ICEEMDAN algorithm,the obtained low-frequency modal components are reconstructed and used as input of the EWT algorithm for decomposition,and the discrete coefficients are used to evaluate the decomposition effect of each iteration of the EWT algorithm.Secondly,accuracy of the ICEEMDAN-EWT algorithm was verified by decomposing a set of simulated signals,and root mean square error(RMSE)of the algorithm was reduced by 5.2%compared with the ICEEMDAN algorithm.Finally,experiments were conducted on a CKA6163A machine tool to identify thermal errors using the five-point method,comparing the ICEEMDAN-EWT separation algorithm with the Fourier transform(FFT)algorithm.The experimental results show that compared with the FFT algorithm,the Pearson correlation of the five thermal deformation signals and machine tool temperature obtained by ICEEMDAN-EWT algorithm is improved by 3.8%and the Spearman correlation improved by 6.6%,indicating the proposed method is with higher accuracy.

关 键 词：数控车床 主轴 热误差 完全自适应噪声集合经验模态分解-经验小波变换(ICEEMDAN-EWT) 误差分离 

分 类 号：TG519.1[金属学及工艺—金属切削加工及机床]