Sensor Fusion Strategy in the Monitoring of Cutting Tool Wear

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Abstract:

Cutting tool wear is a major problem in machining processes. It has a great effect on the quality of a workpiece. Thus, monitoring cutting tool wear is very important in order to maintain the workpiece quality as well to reduce production rate and production time. The use of a single sensor in a monitoring system may not be accurate to detect cutting tool wear. In this paper, sensor fusion technology is introduced for monitoring cutting tool wear.

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Key Engineering Materials (Volumes 306-308)

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727-732

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March 2006

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© 2006 Trans Tech Publications Ltd. All Rights Reserved

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[1] D.E. Dimla Sr., P.M. Lister, On-line metal cutting tool condition monitoring. II: tool-state classification using multi-layer perceptron neural networks, International Journal of Machine Tools & Manufacture 40 (2000) 769-781.

DOI: 10.1016/s0890-6955(99)00085-1

Google Scholar

[2] L.I. Burke, Automated identification of tool wear states in machining processes: an application of self-organizing neural networks. PhD Thesis, Department of Industrial Engineering and Operations Research, UC at Berkeley, USA, (1989).

Google Scholar

[3] T.I. Liu, E.J. Ko, On-line recognition of drill wear via artificial neural networks, in: ASME's Winter Annual Meeting, Monitoring and Control for Manufacturing Processes, 44, PED, 1990, pp.101-110.

Google Scholar

[4] A. Noori-Khajavi, R. Komanduri, Frequency and time domain analyses of sensor signals in drilling-II. Investigation on some problems associated with sensor integration, Int. J. Mach. Tool Manufact. 35 (6) (1995) 795-815.

DOI: 10.1016/0890-6955(94)00061-n

Google Scholar

[5] Dimla E. Dimla Snr, Sensor signals for tool-wear monitoring in metal cutting operations-a review of methods, International Journal of Machine Tools & Manufacture 40 (2000) 1073- 1098.

DOI: 10.1016/s0890-6955(99)00122-4

Google Scholar

[6] R. Tanner, N.K. Loh, A taxonomy of multi-sensor fusion, Journal of Manufacturing Systems 11(5) (1994) 314-325.

Google Scholar

[7] Reddy 1992, Multisensor data fusion: state-of-the-art. Journal of Information Science and Technology1, 91-103.

Google Scholar

[8] P.M. Lister. On-line measurement of tool wear. Ph.D. thesis, Manufacturing and Machine Tools Division, Department of Mechanical Engineering, UMIST, Manchester, UK, (1993).

Google Scholar

[9] H.V. Ravindra, Y.G. Srinivasa, R. Krishnamurthy, Modelling of tool wear based on cutting forces in turning, Wear 169 (1993) 25-32.

DOI: 10.1016/0043-1648(93)90387-2

Google Scholar

[10] T.J. Ko, D.W. Cho, Cutting state monitoring in milling by a neural network, International Journal of Machine Tools and Manufacture 34 (5) (1994) 659-676.

DOI: 10.1016/0890-6955(94)90050-7

Google Scholar

[11] D.A. Dornfeld, Neural network sensor fusion for tool condition monitoring, Annals of the CIRP 39 (1) (1990) 101-105.

DOI: 10.1016/s0007-8506(07)61012-9

Google Scholar

[12] Muslim Mahardika, Zahari Taha, Djoko Suharto, Kimiyuki Mitsui, Hideki Aoyama, Cutting Tool Wear Monitoring of Turning Operations by Neural Network, Proceedings of AUN/SEED Net - 4th Field-wise Seminar for Manufacturing Engineering, Makati City, Philippines 7-10 Oct 2004, page 1-4.

DOI: 10.4028/www.scientific.net/kem.306-308.727

Google Scholar