Research on Chatter Recognition and Suppression Methods for Robotic Milling of Thin-walled Cylinders

doi:10.3969/j.issn.1004-132X.2026.03.013

Abstract

Abstract:

Aiming at the problems that chattering was easy to occur in robotic intermittent grooving milling applications of cast thin-walled cylindrical parts， which led to the degradation of surface quality of the parts， an on-line identification and suppression method of chatter was proposed based on entropy difference of power spectrum and variable rotational speed. Firstly， the type of chatter and the characteristics of different milling states were determined based on the analyses of milling conditions of thin-walled parts. Secondly， the combination of power spectrum entropy difference and root-mean-square value was utilized for milling state identification， and a spindle rotational frequency and the multiplier fast removal algorithm was proposed to address the issues of early chatter frequencies being easily drowned out. Thirdly， combined with a number of experiments to clarify the influences of milling parameters on chatter， the discrete variable speed method was adopted for chatter suppression， and a spindle speed update strategy was developed. Finally， an online chatter monitoring and suppression system was developed， and variable speed flutter suppression tests were conducted. The results show that the method proposed herein effectively identifies the idle， stable and chattering states， and the amplitude of vibration acceleration is reduced by about 37.3% after chattering suppression， and the roughness value of the machined groove bottom surface is reduced by about 34.7%.

Key words: thin-wall component, robotic milling, chatter recognition, chatter suppression

摘要：

针对机器人在铸造类薄壁筒件断续开槽铣削应用中易发生颤振导致零件表面质量降低的问题，提出一种基于功率谱熵差和变转速的颤振在线识别及抑制方法。基于薄壁件铣削工况分析确定颤振类型及不同铣削状态特征；结合功率谱熵差与均方根值进行铣削状态识别，并针对早期颤振频率易被淹没的问题，提出一种主轴转频及其倍频快速去除算法；结合多组试验明确铣削参数对颤振的影响，采用离散变转速法进行颤振抑制并制定主轴转速更新策略；开发了颤振在线监测及抑制系统并进行变转速颤振抑制试验。试验结果表明，该方法可有效识别出空转、稳定和颤振状态，且颤振抑制后振动加速度幅值降低约37.3%，加工槽底表面粗糙度值降低约34.7%。

关键词: 薄壁件, 机器人铣削, 颤振识别, 颤振抑制

CLC Number:

TP249

YI Yali, CHENG Yangyang, CHEN Xiaowei, YANG Wenbo, JIN Herong. Research on Chatter Recognition and Suppression Methods for Robotic Milling of Thin-walled Cylinders[J]. China Mechanical Engineering, 2026, 37(3): 634-644.

宜亚丽, 程阳洋, 陈晓卫, 杨文博, 金贺荣. 薄壁筒件机器人铣削颤振识别及抑制方法研究[J]. 中国机械工程, 2026, 37(3): 634-644.

Figures/Tables 28

Fig.1 Thin-walled cylinder

Fig.2 Robotic milling system

Fig.3 Robotic end-effector

Fig.4 Test piece and milling cutter

Fig.5 Robot milling test bed

Fig.6 Acceleration sensor arrangement

Tab.1 Robot joint angles

	θ₁	θ₂	θ₃	θ₄	θ₅	θ₆
下刀点	-13.43	-80.36	117.50	-0.24	52.79	2.62
退刀点	-15.27	-79.87	116.87	-0.23	52.92	0.77

Fig.7 Time/frequency domain plots of signals in each region

Fig.8 Frequency-domain diagram of displacement in the low-frequency region

Fig.9 Robotic milling system and hammer impact test diagram of thin-walled component

Tab.2 Modal parameters table of robotic milling system for thin-walled components

	模态阶次	频率/Hz	阻尼比/%	振型系数	模态质量/kg
机器人铣削系统	1阶	523.24	1.6	0.033	9.47
	2阶	1407.14	12.4	0.047	0.86
	3阶	1734.22	4.3	0.016	7.27
薄壁件	1阶	1235.14	2.5	0.035	5.71
	2阶	1661.52	1.7	0.017	17.30
	3阶	1836.51	2.6	0.038	5.06

Fig.10 Time/frequency domain plots for different milling states

Fig.11 Comparison of original signal and de-frequency signal

Fig.12 Power spectrum entropy curves

Fig.13 Power spectrum entropy curves of spindle idle state

Fig.14 Root mean square value changing curve

Fig.15 Variable milling depth effect diagram

Fig.16 Frequency domain plots for different milling depths

Fig.17 Plot of chattering frequency and roughness variation

Fig.18 Time and frequency domain plots for different feed rates

Fig.19 Time domain plot for different spindle speeds

Fig.20 Frequency domain comparison of different spindle speeds

Fig.21 Main interface of chattering online monitoring and suppression system

Fig.22 Flutter identification and suppression program flow

Tab.3 Milling test parameters

序号	铣削次数	转速n/ （r·min^-1）	进给v_f/ （mm·min^-1）	切深a_p/ mm	准确识别次数	抑制后状态
1	3	8000	360	0.6	3	稳定
2	3	8000	480	0.8	3	稳定
3	3	12 000	360	0.6	2	稳定
4	3	12 000	480	0.8	3	稳定
5	3	10 000	360	0.6	3	稳定
6	3	10 000	480	0.8	3	稳定

Fig.23 Comparison before and after chatter suppression of machining groove

Fig.24 Signal comparison before and after chatter suppression

Fig.25 Roughness change before and after chatter suppression

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