Teti, Roberto and Baciu, Ioan Liviu (2004) Neural Network Processing of Audible Sound Signal Parameters for Sensor Monitoring of Tool Conditions. In: 4th CIRP Int. Sem. on Intelligent Computation in Manufacturing Engineering – CIRP ICME ‘04, 30 June - 2 July 2004, Sorrento, Italy.

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Tipologia del documento: Contributo a Convegno o Workshop (Paper)
Titolo: Neural Network Processing of Audible Sound Signal Parameters for Sensor Monitoring of Tool Conditions
Teti, Robertoroberto.teti@unina.it
Baciu, Ioan Liviubaciu@unina.it
Data: Giugno 2004
Tipo di data: Pubblicazione
Numero di pagine: 6
URL ufficiale: http://www.icme.unina.it/ICME_04.htm
Tipo di evento: Conference
Titolo dell'evento: 4th CIRP Int. Sem. on Intelligent Computation in Manufacturing Engineering – CIRP ICME ‘04
Luogo dell'evento: Sorrento, Italy
Data dell'evento: 30 June - 2 July 2004
Data: Giugno 2004
Numero di pagine: 6
Parole chiave: Tool condition monitoring, Audible sound sensors, Neural Networks
Riferimenti bibliografici: [1] Birla, S., 1980, Sensors for Adaptive Control and Machine Diagnostics, Technology of Machine Tools – Machine Tool Task Force Report, Vol. 4, Machine Tools Controls, Miskell R.V., ed., LLNL, ReportUcrl-52960, 7.12-1 – 1.12-70. [2] Micheletti, G.F., Koenig, W., Victor, R.H., 1976, In- Process Tool Wear Sensors for Cutting Operations, Annals of CIRP, Vol. 25/2: 483-496. [3] Tlusty, J., Andrews, G.C., 1983, A Critical Review of Sensors for Unmanned Machining, Annals of CIRP, Vol. 32/2: 563-572. [4] Toenshoff, H.K., Wulfsberg, J.P., Kals, H.J.J., Koening, W., Van Luttervelt, C.A.,1988, Development and Trends in Monitoring and Control of Machining Processes, Annals of CIRP, Vol. 37/2: 611-622. [5] Byrne, G., Dornfeld, D., Inasaki, I., Kettler, G., Koenig, W., Teti, R., 1995, Tool Condition Monitoring (TCM) – The Status of Research and Industrial Application, Annals of CIRP, Vol. 44/2: 541-567. [6] Weller, E.J., Schrier, H.M., And Weichbrodt, B., What Sound Can Be Expected From a Worn Tool, ASME J. of Engineering for Industry, Vol. 91: 525-534. [7] Anderson, D., 1988, Method for Monitoring Cutting Tool Wear During a Machining Operation, The Boeing Company, USA, USP. 04744242. [8] Trabelsi, H., Kannatey–Asibu, Jr., E., Pattern – Recognition Analysis of Sound radiation in Metal Cutting, International Journal of Advanced Manufacturing Technology, Vol. 6: 220-231. [9] Delio, T., Tlusty, J., Smith, S., 1992, Use of Audio Sound Signals for Chatter Detection and Control, ASME Journal of Engineering for Industry, Vol. 114: 579-598. [10] Sadat, A.B., Raman, S., 1987, Detection Of Tool Flank Wear Using Acoustic Signature Analysis, WEAR, Vol. 115: 265-272. [11] Adhesive Wear in Machining,” Transactions of NARMI/SME, Vol. XXVIII, pp. 257-262. [12] Lu, M.C., Kannatey-Asibu, Jr. E., Analysis Of Sound Signal Generation Due To Flank Wear In Turning, International ME 2000 Congress & Exposition, Orlando, FL. [13] Larson Davis Lab., 2800 Manual, Preliminary Documentation 1/27/93. [14] Spectrum Pressure Level (Spl 3100), Manual Version 0.98, Program Version 1.10. [15] Noise And Vibration Works, References Manual Version 1.22. [16] Tukey, E.M., 1977, Exploratory Data Analysis, Addison-Wesley, Reading, MA. [17] Hertz, J., Krogh, A., Palmer, R.G., 1991, Introduction to the Theory of Neural Computation, Addison-Wesley, Reading, MA. [18] Fahlman, S.E., Lebiere, C., 1990, An Empirical Study of Learning Speed in Back Propagation Networks, Carnegie Mellon University Technical Report, CMUCS: 88-162. [19] Masters, T., 1993, Practical Neural Networks Recipies in C++, Academic Press, San Diego, CA.
Settori scientifico-disciplinari del MIUR: Area 09 - Ingegneria industriale e dell'informazione > ING-IND/16 - Tecnologie e sistemi di lavorazione
Area 09 - Ingegneria industriale e dell'informazione > ING-INF/05 - Sistemi di elaborazione delle informazioni
Depositato il: 01 Feb 2007
Ultima modifica: 30 Apr 2014 19:24
URI: http://www.fedoa.unina.it/id/eprint/896


The increase of productivity in manufacturing processes largely relies on the successful introduction of flexible automation in machining processes. Such success, in turn, is largely based on the availability of data on the operating conditions, provided by reliable sensing devices. In the present work, experimental verifications of the possibilities of utilizing audible sound based sensing methods for in-process identification of tool conditions are presented for band sawing processes carried out on aluminium alloy and low carbon steel plates.

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