Technical Paper:
Comparing Vibration Sensor Positions in CNC Turning for a Feasible Application in Smart Manufacturing System
Jonny Herwan†, Seisuke Kano, Ryabov Oleg, Hiroyuki Sawada, and Masahiro Watanabe
Advanced Manufacturing Research Institute, National Institute of Advanced Industrial Science and Technology (AIST)
AIST Tsukuba East, 1-2-1 Namiki, Tsukuba, Ibaraki 305-8564, Japan
†Corresponding author
Tool condition monitoring, such as tool wear and breakage, is an essential feature in smart manufacturing system. One of most potential sensors that can be used in tool monitoring is vibration sensor, which usually assembled at tool shank. However, in case of CNC turning with rotating tool turret, it is impossible to assemble the vibration sensor at the tool shank because wire of the sensor will be damaged when the turret rotated. This paper is addressed to compare thoroughly alternative sensor positions. Ten sensor positions including tool shank, as a reference, are investigated. The signals from three types of cutting, namely; normal cutting, abnormal cutting with tool wear and abnormal cutting when tool breakage occurred, are investigated. Based on the magnitude of the output signals and their capability to predict tool wear and breakage, a suggestion on vibration sensor positions is proposed.
- [1] K.-D. Thoben, S. Wiener, and T. Wuest, “Industrie 4.0 and smart manufacturing – A review of research issues and application examples,” Int. J. Automation Technol., Vol.11, pp. 4-16, 2017.
- [2] D. Kokuryo, T. Kaihara, S. S. Kuik, S. Suginouchi, and K. Hirai, “Value co-creative manufacturing with IoT-based smart factory for mass customization,” Int. J. Automation Technol., Vol.11, pp. 509-518, 2017.
- [3] D. Wu, M. J. Greer, D. W. Rosen, and D. Schaefer, “Cloud manufacturing: Strategic vision and state-of-the-art,” J. of Manufacturing Systems, Vol.32, pp. 564-579, 2013.
- [4] S. Wang, R. Badarinath, E. Lehtihet, and V. Prabhu, “Evaluation of additive manufacturing processes in fabrication of personalized robot,” Int. J. Automation Technol., Vol.11, pp. 29-37, 2017.
- [5] H. Hibino, M. Yamamoto, M. Yamaguchi, and T. Kobayashi, “A Study on Lot-Size Dependence of Energy Consumption per Unit of Production Throughput Considering Buffer Capacity,” Int. J. Automation Technol., Vol.11, pp. 46-55, 2017.
- [6] M. Nakamura, S. Makihara, J. Sugiura, and Y. Kamioka, “Dynamic Optimization Production System Based on Simulation Integrated Manufacturing and its Application to Mass Production,” Int. J. Automation Technol., Vol.11, pp. 56-66, 2017.
- [7] A. Caggiano, T. Segreto, and R. Teti, “Cloud manufacturing framework for smart manufacturing of machining,” Procedia CIRP, Vol.55, pp. 248-253, 2016.
- [8] M. Mori, M. Fujishima, M. Komatsu, B. Zhao, and Y. Liu, “Development of remote monitoring and maintenance system for machine tools,” CIRP Annals-Manufacturing Technology, Vol.57, pp. 433-436, 2008.
- [9] R. Gao, L. Wang, R. Teti, D. Dornfeld, S. Kumara, M. Mori, and M. Helu, “Cloud-enable prognosis for manufacturing,” CIRP Annals-Manufacturing Technology, Vol.64, pp. 749-772, 2015.
- [10] S. Wiesner, E. Marilungo, and K.-D. Thoben, “Cyber-physical product-service systems – challenges for requirements engineering,” Int. J. Automation Technol., Vol.11, pp. 17-28, 2017.
- [11] R. Teti, K. Jemielniak, G. O’Donnell, and D. Dornfeld, “Advanced monitoring of machining operations,” CIRP Annals-Manufacturing Technology, Vol.59, pp. 717-732, 2010.
- [12] V. Balsamo, A. Caggiano, K. Jemielniak, J. Kossakowska, M. Nejman, and R. Teti, “Multi sensor signal processing for catastrophic tool failure detection in turning,” Procedia CIRP, Vol.41, pp. 939-944, 2016.
- [13] J.-D. Kim and I.-N. Choi, “Development of a tool failure detection system using multi-sensors,” Int. J. Mach. Tools Manufact., Vol.36, pp. 861-870, 1996.
- [14] L. Dan and H. Mathew, “Tool wear and failure monitoring techniques for turning – A review,” Int. J. Mach. Tools Manufact., Vol.30, pp. 579-598, 1990.
- [15] S.-M. Wang, Y.-S. Chen, C.-Y. Lee, C.-C. Yeh, and C.-C. Wang, “Methods of in-process on-machine auto inspection of dimensional error and auto-compensation of tool wear for precision turning,” Applied Sciences, Vol.6, pp. 1-15, 2016.
- [16] C. Scheffer and P. S. Heyns, “Wear monitoring in turning operations using vibration and strain measurements,” Mechanical Systems and Signal Processing, Vol.15, pp. 1185-1202, 2001.
- [17] D. R. Salgado and F. J. Alonso, “Tool wear detection in turning operations using singular spectrum analysis,” J. of Materials Processing Technology, Vol.171, pp. 451-458, 2006.
- [18] D. E. Dimla Sr. and P. M. Lister, “On-line metal cutting tool condition monitoring. I: force and vibration analyses,” Int. J. Mach. Tools Manufact., Vol.40, pp. 739-768, 2000.
- [19] E. García Plaza and P. J. Núñez López, “Application of the wavelet packet transform to vibration signals for surface roughness monitoring in CNC turning operations,” Mechanical Systems and Signal Processing, Vol.98, pp. 902-919, 2018.
- [20] S. C. Lina and M. F. Chang, “A study on the effects of vibrations on the surface finish using a surface topography simulation model for turning,” Int. J. Mach. Tools Manufact., Vol.38, pp. 763-782, 1998.
- [21] TYPE 4535-B-001 – Brüel & Kjæ r Sound & Vibration. https://www.bksv.com/en/products/transducers/vibration/Vibration-transducers/accelerometers/4535-B-001 [accessed December 1, 2017]
- [22] Hurco Mission Statement. http://www.hurco.com/en-us/about-hurco/overview/pages/default.aspx [accessed October 2, 2017]
- [23] R. Sato, G. Tashiro, and K. Shirase, “Analysis of the coupled vibration between feed drive systems and machine tool structure,” Int. J. Automation Technol., Vol.9, pp. 689-697, 2015.
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