Research on Tool Condition Monitoring System in Auto-Balancing Machines

Abstract:

A drill set working condition monitoring system is introduced and investigated to improve the performance of auto-balancing machine. The experiment setup to acquire data of armature currents of spindle and servo motors is constructed. The features of the armature currents of both spindle motor and servo motor in time domain, frequency domain and time-frequency domain are extracted respectively, and they are fused separately by two distinct RBF ANNs to get the primary fusing results. The primary results are fused by the third RBF ANN to get a comprehensive result. Experiment results demonstrate that the servo motor current has a closer relation with drill set working condition than that of spindle motor, and two successive fusing operations can achieve more reliable recognition with a correctness up to 86.67%.

• Periodical: Key Engineering Materials (Volume 437)
• Pages: 497-501
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.437.497
• Online since: May 2010
• Authors: Ding Ding Zhao, Ping Cai
• Keywords: Data Fusion, Servo Motor, Spindle Motor, Tool Condition Monitoring

References

[1] S. Braun, E. Lenz, C.L. Wu: Signature analysis applied to drilling. Journal Mechanical Design, Transactions of the ASME. 104 (1982), pp.268-276.

DOI: 10.1115/1.3256335

[2] E. Jantunen, H. Jokinen: Automated on-line diagnosis of cutting tool condition (second version). International journal of flexible automation and integrated manufacturing. 4 (3-4) (1996), pp.273-287.

[3] Erkki Jantunen: A summary of methods applied to tool condition monitoring in drilling. International Journal of Machine Tools & Manufacture. 42 (2002), pp.997-1010.

DOI: 10.1016/s0890-6955(02)00040-8

[4] Ismet Kandilli, Murat Sonmez, Huseyin Metin Ertunc, Bekir Cakir: Online monitoring of tool wear in drilling and milling by multi-sensor neural network fusion. Proceeding of the 2007 IEEE, International Conference on Mechatronics and Automation (August 5-8, 2007).

DOI: 10.1109/icma.2007.4303752

[5] Peng-Yang Li, Ying-Wu Fang, Yi Wang, Ming-Shun Yang: Time-frequency analyses for cutting tools wear characteristics. Proceeding of the Fifth International Conference on Machine Learning and Cybernetics. Dalian, 13-16 August (2006).

DOI: 10.1109/icmlc.2006.258465

[6] Kang-Ning Lou, Cheng-Jen Lin: An intelligent sensor fusion system for tool monitoring on a machining center. Proceeding of the 1996 IEEE/SICE/RSJ International conference on Multisensor fusion and integration for intelligent systems (1996).

DOI: 10.1109/mfi.1996.572179

[7] Karali Patra, Surjya K. Pal. Kingshook Bhattacharyya: Drill wear monitoring through current signature analysis using wavelet packet transform and artificial neural network. IEEE International Conference on Industrial Technology (ICIT 2006).

DOI: 10.1109/icit.2006.372577

[8] Xiaoli Li, Alexander Djordjevich, Patri K. Venuvinod: Current-Sensor-Based feed cutting force intelligent estimation and tool wear condition monitoring. IEEE transactions on Industrial Electronics. Vol. 47 (June 2000), No. 3.

DOI: 10.1109/41.847910