A Milling Stability Determining Method Considering the System Static-dynamic Characteristics and Time-varying Tool Wear

doi:10.3969/j.issn.1004-132X.2026.07.007

Abstract

Abstract:

Increasing milling parameters improves machining efficiency but tends to cause chatter. To address this issue， this paper proposes a stability identification method that considers the static and dynamic characteristics of the milling process as well as the influence of timevarying tool wear. First， a threedimensional stability lobe diagram， incorporating radial cutting depth and tool diameter， is established based on the milling force model to determine the basic machining parameters. Second， frequencydomain signal processing is adopted to eliminate interference from spindle rotational frequency and to identify the actual stability state. Meanwhile， tool wear is monitored based on variations in cutting edge coefficients， and the wear factor is integrated into the identification system. Online identification test results verify that the proposed method can increase the material removal rate while ensuring process stability.

Key words: milling stability, material removal rate, tool wear, dynamic characteristics

摘要：

增大铣削加工参数可提高加工效率，却易产生加工颤振的问题，因此提出一种同时考虑铣削静动态性能和时变刀具磨损影响的稳定性判别方法。先依托铣削力模型建立考虑径向切削深度与刀具直径的三维铣削叶瓣稳定图，确定基础加工参数。再通过频域信号处理剔除主轴转频干扰，判别实际加工的铣削稳定性；同时基于铣削刃口系数变化监测刀具磨损，并将磨损因素融入判别体系。铣削稳定性在线判别实验结果表明，所提方法在保证工艺稳定的前提下提高了材料去除率。

关键词: 铣削稳定性, 材料去除率, 刀具磨损, 动态判别

CLC Number:

TG50

GUO Miaoxian, HAN Chentong, GUO Weicheng, DING Zishan, WEI Yan, ZHOU Jinqiang. A Milling Stability Determining Method Considering the System Static-dynamic Characteristics and Time-varying Tool Wear[J]. China Mechanical Engineering, 2026, 37(7): 1587-1594.

郭淼现, 韩琛潼, 郭维诚, 丁子珊, 魏燕, 周金强. 考虑铣削静动态特性及时变刀具磨损的铣削稳定性判别方法[J]. 中国机械工程, 2026, 37(7): 1587-1594.

Figures/Tables 16

Fig.1 Stability lobe diagram of three-dimensional milling process

Fig.2 Three-dimensional stability lobe diagram with material removal rate

Fig.3 Milling experimental platform

Tab.1 Milling process parameters of verification tests

实验	n/（r·min^-1）	a_e/mm	a_p/mm	v/（mm·min^-1）	工件材料
Test1	7200	5	2.5	1000	铝合金
Test2	7200	5	5	1000	铝合金
Test3	7200	5	7.5	1000	铝合金
Test4	7200	5	10	1000	铝合金
Test5	1820	7	0.5	182	钛合金

Fig.4 Frequency domain distribution of milling force in single-factor experiments

Fig.5 Filter for milling stability identification

Fig.6 Milling stability identification under filtering conditions

Fig.7 Dynamic calculation results of milling coefficients

Fig.8 Comprehensive milling force coefficients and corresponding tool wear

Tab.2 Milling force coefficients corresponding to different tool wear levels

刀具后刀面磨损V_B/μm	109	188	288	560
切向铣削力系数k_tc/（N·mm^-2）	1502	2109	2410	3868
径向铣削力系数k_rc/（N·mm^-2）	619	613	609	727

Fig.9 Stability lobe diagrams for different flank wear values of cutting tools

Fig.10 Dynamic identification flowchart of milling stability

Fig.11 Process adaptability verification of dynamic identification for milling stability

Fig.12 Unstable milling signals obtained in Test 6 experiment

Tab.3 Milling force coefficients at different milling durations

铣削加工时间/s	切向铣削力系数k_tc/（N·mm^-2）	径向铣削力系数k_rc/（N·mm^-2）
0	704	546
428	875	715
1981	1649	1390
2605	2198	1399
2977	2523	1586

Tab.4 Comparison of machining time and material removal amount between test 5 and test 6

实验		主轴转速（r·min^-1）	径向切削深度/mm	轴向切削深度/mm	进给速度/（mm·min^-1）	加工时长/s	材料去除量/mm³
Test5		1820	7	0.5	182	4015	42 625.9
Test6	Ⅰ~Ⅱ（0~428 s）	2712	5.6	1.02	271	428	11 042.1
	Ⅱ~Ⅲ（428~1981 s）	2702	4.2	1.08	270	1553	31 699.8
	Ⅲ~Ⅳ（1981~2605 s）	2691	5.6	0.55	269	624	8616.6
	Ⅳ~Ⅴ（2605~2977 s）	2691	5.6	0.48	269	372	4483.0
	Ⅴ~Ⅵ（2977~3500 s）	2691	4.2	0.5	269	/	/
Test6综合刃口系数小于120 N/mm的总计加工时长及材料去除量						2977	55841.5

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