Advanced SearchSearch Tips
Tool Breakage Detection Using Feed Motor Current
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Tool Breakage Detection Using Feed Motor Current
Jeong, Young Hun;
  PDF(new window)
Tool condition monitoring plays one of the most important roles in the improvement of both machining quality and productivity. In this regard, various process signals and monitoring methods have been developed. However, most of the existing studies used cutting force or acoustic emission signals, which posed risks of interference with the machining system in dynamics, fixturing, and machining configuration. In this study, a feed motor current signal is used as a process signal representing process and tool states in tool breakage monitoring based on an adaptive autoregressive model and unsupervised neural network. From the experimental results using various cases of tool breakage, it is shown that the developed system can successfully detect tool breakage before two revolutions of the spindle after tool breakage.
Feed Motor Current;Milling Process;Tool Condition Monitoring;Tool Breakage;
 Cited by
초음파 적외선열화상 기법을 적용한 모터 코어의 신뢰성 평가,정윤수;노치성;이경일;김재열;

한국기계가공학회지, 2016. vol.15. 4, pp.60-66 crossref(new window)
프로피버스 통신을 이용한 실시간 절삭 상태 모니터링에 관한 연구,윤상환;조상필;류성기;

한국기계가공학회지, 2016. vol.15. 3, pp.1-7 crossref(new window)
Liang, S. Y., Hecker, R. L. and Landers, R. G., "Machining Process Monitoring and Control: The State-of-the-Art," J. Manuf. Sci. Eng., Vol. 126, No. 2, pp. 297-310, 2004. crossref(new window)

Won, J.-S., Lim, E.-S. and Jung, Y.-G., "Estimation of Machinability for Super Heat-resistant Alloys Inconel 600 in Turning Process," J. Korean Soc. Manuf. Process Eng., Vol. 10, No. 6, pp. 1-8, 2011.

Byrne, G., Dornfeld, D., Inasaki, I., Ketteler, G., Konig, W. and Teti, R., "Tool Condition Monitoring (TCM)-The Status of Research and Industrial Application," CIRP Ann-Manuf. Technol., Vol. 44, No. 2, pp. 541-567, 1995. crossref(new window)

Kim, S.-H. and Baek, W.-B., "Tool Monitoring System using Vision System with Minimizing External Condition," J. Korean Soc. Manuf. Process Eng., Vol. 11, No. 5, pp. 142-147, 2012.

Maeng, M.-J., "Fracture Detection of Milling Cutter Using Cutting Force and Acoustic Emission Signals," J. Korean Soc. Manuf. Process Eng., Vol. 3, No. 1, pp. 28-37, 2004.

Ko, T. J. and Cho, D.-W., "Cutting State Monitoring in Milling by a Neural Network," Int. J. Mach. Tools Manuf., Vol. 34, No. 5, pp. 659-676, 1994. crossref(new window)

Ko, T. J., Cho, D.-W. and Jung, M. Y., "On-Line Monitoring of Tool Breakage in Face Milling Using a Self-Organized Neural Network," J. Manuf. Syst., Vol. 14, No. 2, pp. 80-90, 1995. crossref(new window)

Lee, J. M., Choi, D. K., Kim, J. and Chu, C. N., "Real-Time Tool Breakage Monitoring for NC Milling Process," CIRP Ann-Manuf. Technol. Vol. 44, No. 1, pp. 59-62, 1995. crossref(new window)

Li, W., Dong, S. and Yuan, Z., "Discrete Wavelet Transform for Tool Breakage Monitoring," Int. J. Mach. Tools Manuf., Vol. 39, No. 12, pp. 1935-1944, 1999. crossref(new window)

Jeong, Y. H. and Cho, D.-W., "Estimating Cutting Force from Rotating and Stationary Feed Motor Currents on a Milling Machine," Int. J. Mach. Tools Manuf., Vol. 42 No. 14, pp.1559-1566, 2002. crossref(new window)

Yoon, M.-C., Kim, Y.-G. and Kim, K.-H., "The Characteristics and Stability Boundary Analysis of Chatter using Neural Network," J. Korean Soc. Manuf. Process Eng., Vol. 5, No. 2, pp. 16-21, 2006.

Grossberg, S., "Adaptive Pattern Classification and Universal Recoding: I. Parallel Development and Coding of Neural Feature Detectors," Biol. Cybern., Vol. 23 No. 3, pp. 121-134, 1976. crossref(new window)