An online monitoring methodology for grinding state identification based on real-time signal of CNC grinding machine

被引:13
作者
Li, Gan [1 ]
Bao, Yan [1 ]
Wang, Hao [1 ]
Dong, Zhigang [1 ]
Guo, Xiaoguang [1 ]
Kang, Renke [1 ]
机构
[1] Dalian Univ Technol, State Key Lab High performance Precis Mfg, Dalian 116024, Peoples R China
基金
中国国家自然科学基金;
关键词
Grinding machine; Real-time monitoring; Grinding forces; Surface topography; CNC controller; WHEEL WEAR; TOOL WEAR; CUTTING FORCE; SYSTEM; POWER;
D O I
10.1016/j.ymssp.2023.110540
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
The grinding state is closely related to machining accuracy, wheel wear, and material removal efficiency. Changes in the grinding state often mean instability of the processing state, which will cause additional costs such as wheel wear and a decline in the processing quality of the workpiece. Various methods for monitoring grinding conditions have been proposed in the past, but none of these methods have been universally successful due to the complex nature of the machining processes. This research presents a new method for real-time monitoring of grinding forces and workpiece surface topography during grinding processing using real-time signals from the computer numerical control (CNC) system of the grinding machine without any additional sensors. By extracting real-time signals during the grinding process for time and frequencydomain analysis, the grinding state can be identified online. Based on this method, the time-frequency domain calibration experiment is carried out. The resolution of the time-domain calibration results reached 6e-5N, which can characterize the real-time change of grinding force during the grinding process. The frequency-domain analysis can achieve real-time monitoring of the spindle state of the workpiece and the grinding spindle state and obtain the frequency-domain transmission path under different processing conditions. The workpiece surface morphology is estimated in real-time using the feedback signal of the grinding machine, and the results are verified in mm, & mu;m, and nm scales. The test results show that the use of real-time signals from the grinding machine to monitor the grinding state has the advantages of high precision, reliability, and convenient implementation.
引用
收藏
页数:23
相关论文
共 48 条
[1]   Predictive modeling of force and power based on a new analytical undeformed chip thickness model in ceramic grinding [J].
Agarwal, Sanjay ;
Rao, P. Venkateswara .
INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, 2013, 65 :68-78
[2]   Laser conditioning and structuring of grinding tools - a review [J].
Azarhoushang, Bahman ;
Zahedi, Ali .
ADVANCES IN MANUFACTURING, 2017, 5 (01) :35-49
[3]   Factors affecting wheel collapse in grinding [J].
Badger, J. ;
Webster, J. .
CIRP ANNALS-MANUFACTURING TECHNOLOGY, 2009, 58 (01) :307-310
[4]   Grinding of sub-micron-grade carbide: Contact and wear mechanisms, loading, conditioning, scrubbing and resin-bond degradation [J].
Badger, Jeffrey .
CIRP ANNALS-MANUFACTURING TECHNOLOGY, 2015, 64 (01) :341-344
[5]   Multiscale modelling of indentation in FCC metals: From atomic to continuum [J].
Chang, Hyung-Jun ;
Fivel, Marc ;
Rodney, David ;
Verdier, Marc .
COMPTES RENDUS PHYSIQUE, 2010, 11 (3-4) :285-292
[6]   An online belt wear monitoring method for abrasive belt grinding under varying grinding parameters [J].
Cheng, Can ;
Li, Jianyong ;
Liu, Yueming ;
Nie, Meng ;
Wang, Wenxi .
JOURNAL OF MANUFACTURING PROCESSES, 2020, 50 :80-89
[7]   Early detection of fatigue damage on rolling element bearings using adapted wavelet [J].
Chiementin, Xavier ;
Bolaers, Fabrice ;
Dron, Jean-Paul .
JOURNAL OF VIBRATION AND ACOUSTICS-TRANSACTIONS OF THE ASME, 2007, 129 (04) :495-506
[8]   Review on grinding-induced residual stresses in metallic materials [J].
Ding, Wenfeng ;
Zhang, Liangchi ;
Li, Zheng ;
Zhu, Yejun ;
Su, Honghua ;
Xu, Jiuhua .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2017, 88 (9-12) :2939-2968
[9]   Titanium alloy microstructure fingerprint plots from in-process machining [J].
Fernandez, D. Suarez ;
Wynne, B. P. ;
Crawforth, P. ;
Jackson, M. .
MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING, 2021, 811
[10]   Using machining force feedback to quantify grain size in beta titanium [J].
Fernandez, Daniel Suarez ;
Jackson, M. ;
Crawforth, P. ;
Fox, K. ;
Wynne, B. P. .
MATERIALIA, 2020, 13