虑及颤振在线抑制的机床高效自适应加工技术研究

doi:10.3901/JME.2024.06.104

机械工程学报 ›› 2024, Vol. 60 ›› Issue (6): 104-113.doi: 10.3901/JME.2024.06.104

• 特邀专栏：数据-知识混合驱动的智能制造系统 • 上一篇下一篇

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虑及颤振在线抑制的机床高效自适应加工技术研究

刘阔¹, 姜业明¹, 黄任杰¹, 李明禹¹, 陈虎², 王永青¹

1. 大连理工大学高性能精密制造全国重点实验室大连 116024;
2. 科德数控股份有限公司大连 116600

收稿日期:2023-09-15 修回日期:2023-12-06 出版日期:2024-03-20 发布日期:2024-06-07
通讯作者: 刘阔，男，1983年出生，教授。主要研究方向为数控机床及产线智能监控技术、数控机床精度保持理论与技术、数控机床热误差实时补偿技术等。E-mail：liukuo@dlut.edu.cn
作者简介:姜业明，男，1995年出生，博士研究生。主要研究方向为切削加工过程在线监测与智能调控技术。E-mail：jiangyeming@mail.dlut.edu.cn
基金资助:
国家自然科学基金(U22B2085)、辽宁省科技重大专项(2020JH1/ 10100016，2021JH1/10400102)和辽宁省“兴辽英才计划” (XLYC1807081)资助项目。

Research on High-efficiency Adaptive Machining Technology of Machine Tools Considering Online Chatter Suppression

LIU Kuo¹, JIANG Yeming¹, HUANG Renjie¹, LI Mingyu¹, CHEN Hu², WANG Yongqing¹

1. State Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology, Dalian 116024;
2. Kede CNC Co., Ltd., Dalian 116600

Received:2023-09-15 Revised:2023-12-06 Online:2024-03-20 Published:2024-06-07

摘要/Abstract

摘要： 针对传统加工方式面对加工余量不均匀、去除量大的复杂结构件缺乏参数调控能力，难以提升加工效率的问题，提出一种虑及颤振在线抑制的机床高效自适应加工技术。首先，基于模糊理论开发二维模糊控制器，通过采集切削过程主轴功率信号，实时调节进给倍率来实现自适应加工。其次，以加权小波包熵为特征量，研究切削颤振在线识别方法，并建立切削系统单自由度模型，通过寻找加工系统不发生颤振的最佳主轴转速，依据颤振频率和刀齿数，实现基于变主轴转速的颤振抑制。然后，基于机床主轴电机功率、扭矩和转速关系，制定主轴转速和进给速度的调控策略，实现虑及颤振在线抑制的机床高效自适应加工。最后，通过两组切削对比试验对该技术进行验证，结果表明，所提高效自适应加工技术可以明显提升加工效率，并能抑制加工过程中的切削颤振。

关键词: 自适应加工, 颤振识别, 颤振抑制, 模糊控制, 加权小波包熵

Abstract: In the field of machining, there are a large number of complex structural parts with uneven machining allowances and large removals. Traditional machining methods lack the ability to control parameters and it is difficult to improve machining efficiency. A high-efficiency adaptive machining technology of machine tools considering online chatter suppression is proposed. First, based on fuzzy theory, a two-dimensional fuzzy controller is developed, which realizes the adaptive machining by collecting the spindle power signal during the machining process and adjusting the feed rate override in real time. Secondly, taking the weighted wavelet packet entropy as the characteristic value, the online identification method of cutting chatter is studied. A single-degree-of-freedom model of the machining system is established. By finding the optimal spindle speed without chattering in the machining system, based on the chatter frequency and the number of teeth, the chatter suppression based on the variable spindle speed is realized. Then, based on the relationship between the power, torque and speed of the spindle motor of the machine tool, the control strategy of the spindle speed and feed speed is formulated. And the high-efficiency adaptive machining of machine tools considering online chatter suppression is realized. Finally, the technology was verified by two sets of cutting comparison tests. The result shows that the technology can significantly improve the machining efficiency and suppress the cutting chatter during machining.

Key words: adaptive machining, chatter identification, chatter suppression, fuzzy control, weighted wavelet packet entropy

中图分类号:

TG156

刘阔, 姜业明, 黄任杰, 李明禹, 陈虎, 王永青. 虑及颤振在线抑制的机床高效自适应加工技术研究[J]. 机械工程学报, 2024, 60(6): 104-113.

LIU Kuo, JIANG Yeming, HUANG Renjie, LI Mingyu, CHEN Hu, WANG Yongqing. Research on High-efficiency Adaptive Machining Technology of Machine Tools Considering Online Chatter Suppression[J]. Journal of Mechanical Engineering, 2024, 60(6): 104-113.

参考文献

[1] WU B H，ZHANG Y，LIU G X，et al. Feedrate optimization method based on machining allowance optimization and constant power constraint[J]. International Journal of Advanced Manufacturing Technology，2021(115)：3345-3360.
[2] MASORY O. Real-time estimation of cutting process parameters in turning[J]. Journal of Engineering for Industry，1984，106(3)：218-221.
[3] HWANG C L. Adaptive turning force control with optimal robustness and constrained feed rate[J]. International Journal of Machine Tools and Manufacture，1993，33(3)：483-493.
[4] ALLEN R，HUANG K. Self-tuning control of cutting force for rough turning operations [J]. Proceedings of the Institution of Mechanical Engineers，Part B：Journal of Engineering Manufacture，1994，208(3)：157-165.
[5] HUANG S J，CHIOU K C. The application of neural networks in self-tuning constant force control[J]. International Journal of Machine Tools and Manufacture，1996，36(1)：17-31.
[6] BEDINI R，PINOTTI P C. Experiments on adaptive constrained control of a CNC lathe[J]. ASME Journal of Engineering for Industry，1982，104：139-149.
[7] 陈琳，黄旭丰，刘梦，等. 综合多约束条件优化连续轨迹前瞻算法[J]. 机械工程学报，2019，55(13)：151-159. CHEN Lin，HUANG Xufeng，LIU Meng，et al. Optimized continuous trajectory look-ahead algorithm with comprehensive multi-constraints[J]. Journal of Mechanical Engineering，2019，55(13)：151-159.
[8] 李辅翼，高宏力，钱桃林. 基于恒主轴电流的机床自适应控制系统的设计[J].机床与液压，2018，46(4)：130-133. LI Fuyi，GAO Hongli，QIAN Taolin. Design of adaptive control system of machine tool based on constant principal axis current[J]. Machine Tool & Hydraulics，2018，46(4)：130-133.
[9] 黄华，李爱平，林献坤. 基于恒功率约束的自适应加工控制系统开发[J]. 同济大学学报，2008(7)：956-961. HUANG Hua，LI Aiping，LIN Xiankun. Fuzzy control system for milling machining[J]. Journal of Tongji University，2008(7)：956-961.
[10] HINTON G E，OSINDERO S，TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation，2006，18(7)：1527-1554.
[11] DESELAERS T，HASAN S，BENDER O，et al. A deep learning approach to machine transliteration[C]// Proceedings of the Fourth Workshop on Statistical Machine Translation，2009：233-241.
[12] CUBAS J，LEORO J，REYES D，et al. Cutting force monitoring and control system for CNC lathe machines[C]//2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM). IEEE，2016：184-188.
[13] HUANG S J，SHY C Y. Fuzzy logic for constant force control of end milling[J]. IEEE Transactions on Industrial Electronics，1999，46(1)：169-176.
[14] ZUPERL U，CUS F，REIBENSCHUH M. Modeling and adaptive force control of milling by using artificial techniques[J]. Journal of Intelligent Manufacturing，2012，23(5)：1805-1815.
[15] 安华，王喆，王国锋，等. 复合材料钻削表面粗糙度在线监测与加工参数自适应优化[J].机械工程学报，2020，56(2)：27-34，42. AN Hua，WANG Zhe，WANG Guofeng，et al. Research on on-line monitoring of surface roughness in composite drilling and adaptive optimization of parameters[J]. Journal of Mechanical Engineering，2020，56(2)：27-34，42.
[16] YAO Z，MEI D，CHEN Z. On-line chatter detection and identification based on wavelet and support vector machine[J]. Journal of Materials Processing Technology，2010，210(5)：713-719.
[17] 王志学，刘献礼，李茂月，等.切削加工颤振智能监控技术[J].机械工程学报，2020，56(24)：1-23. WANG Zhixue，LIU Xianli，LI Maoyue，et al. Intelligent monitoring and control technology of cutting chatter [J]. Journal of Mechanical Engineering，2020，56(24)：1-23.
[18] TANSEL I N，LI M，DEMETGUL M，et al. Detecting chatter and estimating wear from the torque of end milling signals by using index based reasoner(IBR)[J]. The International Journal of Advanced Manufacturing Technology，2012，58(1-4)：109-118.
[19] WANG C X，ZHANG X W，LIU J X，et al. Adaptive vibration reshaping based milling chatter suppression[J]. International Journal of Machine Tools and Manufacture，2019(141)：30-35.
[20] ALBERTELLI P，BRAGHIERI L，TORTA M，et al. Development of a generalized chatter detection methodology for variable speed machining[J]. Mechanical Systems and Signal Processing，2019，123(15)：26-42.
[21] MUHAMMAD B B，WAN M，LIU Y，et al. Active damping of milling vibration using operational amplifer circuit[J]. Chinese Journal of Mechanical Engineering，2018，31：90.
[22] LI X H，LIU S J，WAN S K. et al. Active suppression of milling chatter based on LQR-ANFIS[J]. International Journal of Advanced Manufacturing Technology，2020(111)：2337-2347.
[23] YEH L J，LAI G J. A study of the monitoring and suppression system for turning slender workpieces[J]. Proceedings of the Institution of Mechanical Engineers，Part B：Journal of Engineering Manufacture，1995，209(3)：227-236.
[24] 熊振华，孙宇昕，丁龙杨. 智能车床的颤振实时辨识与在线抑制系统研究[J]. 机械工程学报，2018，54(17)：85-93. XIONG Zhenhua，SUN Yuxin，DING Longyang. Online chatter detection and suppression system for intelligent machine tool[J]. Journal of Mechanical Engineering，2018，54(17)：85-93.
[25] 李茂月，韩振宇，富宏亚，等. 基于开放式控制器的铣削颤振在线抑制[J]. 机械工程学报，2012，48(17)：172-182. LI Maoyue，HAN Zhenyu，FU Hongya，et al. Online milling chatter suppression based on open architecture controller[J]. Journal of Mechanical Engineering，2012，48(17)：172-182.

虑及颤振在线抑制的机床高效自适应加工技术研究

Research on High-efficiency Adaptive Machining Technology of Machine Tools Considering Online Chatter Suppression

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