基于动态特性和小波包的铣削颤振识别

Milling Chatter Recognition Based on Dynamic and Wavelet Packet Decomposition

张春雨 刘长福 朱晓丹 于新丽 罗星辰 陆天昊 李冰杰

Chunyu ZHANG Changfu LIU Xiaodan ZHU Xinli YU Xingchen LUO Tianhao LU Bingjie LI

辽宁石油化工大学 机械工程学院，辽宁 抚顺 113001 辽宁石油化工大学 人工智能与软件学院，辽宁 抚顺 113001

School of Mechanical Engineering，Liaoning Petrochemical University，Fushun Liaoning 113001，China School of Artificial Intelligence and Software，Liaoning Petrochemical University，Fushun Liaoning 113001，China

在金属铣削尤其是低刚度工件加工过程中，颤振是影响工件表面质量、加工效率和刀具寿命等的关键因素。为了避免加工产生的颤振，从信号处理的角度出发，提出了一种基于系统动态特性和小波包的铣削颤振识别方法。通过模态实验获取系统的模态参数，依据颤振频率在系统固有频率附近会出现峰值的特点，采用小波包对原始切削力信号进行分解，然后选取包含丰富颤振信息的频段进行重构，最后对比和分析铣削力信号时频谱图和希尔伯特频谱，实现颤振识别，并对所提出的方法进行了实验验证。结果表明，所提出的方法具有有效性和可靠性。

In the process of metal milling, especially in the machining of low?stiffness workpieces, chatter is a key factor affecting many aspects such as surface quality, machining efficiency and tool life. In order to avoid the chatter, a milling chatter recognition method based on dynamic and Wavelet Packet Decomposition(WPD) is proposed from the signal processing. The modal parameters of the system are obtained by modal experiments. Based on the principle that the chatter frequency will peak near the natural frequency of the system, the original milling force signal is decomposed by WPD, and the sub?signals containing rich chatter information are selected for signal reconstruction. Finally, the time?frequency and Hilbert spectrum characteristics of the reconstructed signal are compared and analysed, and the chatter recognition is performed. At last, the proposed method is verified by the experiments. The results demonstrate the effectiveness and reliability of the proposed method.

辽宁省自然科学计划（博士启动）项目(2022?BS?293);辽宁石油化工大学科研启动基金项目(2021XJJL?005);辽宁省大学生创新创业训练计划项目(202110148017)

10.12422/j.issn.1672-6952.2023.06.012