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

doi:10.3901/JME.2024.06.104

Abstract

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

CLC Number:

TG156

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.

References

[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.