Page 84 - 《应用声学》2023年第4期

第 42 卷 第 4 期 Vol. 42, No. 4
2023 年 7 月 Journal of Applied Acoustics July, 2023

⋄ 研究报告 ⋄

超声辅助加工系统的刀具状态自感知算法 ∗

桑汉德 1,2 陈 爽 1† 张家豪 2† 赵 夙 2 李荣和 1,2

(1 江西理工大学机电工程学院 赣州 341000)
(2 中国科学院宁波材料技术与工程研究所 宁波 315200)

摘要：超声波振动台内含压电材料，可以拾取切削过程产生的振动信号，实现不借助外部传感器刀具工作状态
的自感知。为了从刀具振动信号中获取有效信息，该文提出一种基于经验模态分解的时频域重构算法。首先，
采用经验模态分解算法将原始信号分解，得到多个固有模态函数分量和残差分量；其次，计算原始信号与各分
量之间的时频域互相关系数；再次，归一化时频域互相关系数作为权重值，将固有模态函数分量和残差进行重
构；最后，通过数值仿真和超声辅助加工实验，验证了基于经验模态分解的时频域重构算法的去噪性能，提取
了信噪比为 5.03 dB 的目标信号，从而实现了超声辅助加工系统的自感知功能。
关键词：超声辅助加工；自感知技术；经验模态分解；互相关系数；时频域权重
中图法分类号: TB559 文献标识码: A 文章编号: 1000-310X(2023)04-0746-10
DOI: 10.11684/j.issn.1000-310X.2023.04.010

Tool state self-sensing algorithm used for ultrasonic assisted machining system

SANG Hande 1,2 CHEN Shuang 1 ZHANG Jiahao 2 ZHAO Su 2 LI Ronghe 1,2

(1 School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China)
(2 Ningbo Institute of Materials Technology & Engineering, Chinese Academy of Sciences, Ningbo 315200, China)

Abstract: Due to containing piezoelectric material, the ultrasonic vibration table can pick up the vibration
signal generated by the cutting process, and realize the self-sensing of the working state of the tool without
additional sensors. In order to extract the active ingredients from the tool vibration signal, a time and frequency
domain reconstruction algorithm based on empirical mode decomposition (TF-EMD) is proposed. Firstly, the
original signal is decomposed into multiple intrinsic mode function (IMF) components and the residual by
empirical mode decomposition algorithm. Secondly, the cross-correlation coefficients are calculated between
the original signal and decomposed results in both time domain and frequency domain. Thirdly, the weighted
factors are obtained by normalizing cross-correlation coefficients, and the IMF components and residual are
reconstructed through the obtained weighted factors. Finally, numerical simulation and ultrasonic assisted
machining experiment are carried out to verify the denoising performance of the TF-EMD algorithm. The
signal with a signal-to-noise ratio of 5.03 dB is extracted, thus the self-sensing of the ultrasonic assisted
machining system is realized.
Keywords: Ultrasonic assisted machining; Self-sensing technology; Empirical mode decomposition; Cross-
correlation coefficient; Time-frequency domain weighted factors

2022-04-06 收稿; 2022-06-20 定稿
浙江省 “尖兵”“领雁” 研发攻关计划项目 (2022C01114), 宁波市 3315 创新团队超声冲击处理技术与装备项目 (Y80929DL04), 浙江省
∗
自然科学基金项目 (LQ22E010011), 宁波市自然科学基金项目 (202003N4356, 2021J221)
作者简介: 桑汉德 (1998– ), 男, 山东潍坊人, 硕士研究生, 研究方向: 信号处理与信息融合。
† 通信作者 E-mail: chenshuang826@126.com; zhangjiahao@nimte.ac.cn