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Evaluation of sensor-based condition monitoring methods as in-process tool wear and breakage indices - case study: drilling

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Today, effective unmanned machining operations and automated manufacturing are unthinkable without tool condition monitoring (TCM). Undoubtedly, the implementation of an adaptable, reliable TCM and its successful employment in industry, emerge as major instigations over the recent years. In this work, a sensor-based approach was deployed for the in-process monitoring and detection of tool wear and breakage in drilling. In particular, four widely reported indirect methods for tool wear monitoring, i.e. vibration signals together with thermal signatures, spindle motor and feed motor current measurements were obtained during numerous drillings, under fixed conditions. The acquired raw data was, then, processed both statistically and in the frequency domain, in order to distinguish the meaningful information. The study of the latter is influential in identifying the trend of specific signals toward tool wear mechanism. The efficiency of this information as a tool wear and/or breakage index is the feature that determines the effectiveness and reliability of a potential indirect TCM approach based on a multisensor integration. The paper concludes with a discussion of both advantages and limitations of this effort, stressing the necessity to develop simple, fast condition monitoring methods which are, generally, less likely to fail.
Czasopismo
Rocznik
Tom
Strony
3--13
Opis fizyczny
Bibliogr. 27 poz., rys.
Twórcy
  • Democritus University of Thrace, School of Engineering Department of Production Engineering and Management Faculty of Materials, Processes and Engineering Xanthi, 67100, Thrace, Greece Telephone (and Telefax): +302541079878, itsanaka@ee.duth.gr
Bibliografia
  • [1] Abu-Mahfouz, I., “Drilling Wear Detection and Classification Using Vibration Signals and Artificial Neural Network” International Journal of Machine Tools & Manufacture, vol. 43, pp. 707-720, 2003.
  • [2] Botsaris, P. N., Tsanakas, J. A., State-of-the-art in Methods Applied to Tool Condition Monitoring (TCM) in Unmanned Machining Operations: A Review” 21st International Congress on Condition Monitoring and Diagnostic Engineering Management - COMADEM, 2008.
  • [3] Ertunc, H. M., Oysu, C., “Drill Wear Monitoring Using Cutting Force Signals” Mechatronics, vol. 14, pp. 533-548, 2004.
  • [4] Liu, T. I., Anantharaman, K. S., “Intelligent Classification and Measurement of Drill Wear” Journal of Engineering for Industry, vol. 116, pp. 392-397, 1994.
  • [5] Thangaraj, A., Wright, P. K., “Machining” Robotics Computer-Integrated Mfg., vol. 4, pp. 429-435, 1988.
  • [6] Rehorn, A. G., Jiang, J., Orban, P. E., “State-of the-art Methods and Results in Tool Condition Monitoring: A Review” International Journal of Advanced Manufacturing Technology, vol. 26, pp. 693-710, 2005.
  • [7] Jantunen, E., “A Summary of Methods Applied to Tool Condition Monitoring in Drilling” International Journal of Machine Tools & Manufacture, vol. 42, pp. 997-1010, 2002.
  • [8] Jantunen, E., “Indirect Multisignal Monitoring and Diagnosis of Drill Wear” VTT publications, Espoo, Finland, 2006.
  • [9] Dimla, D. E., “The Correlation of Vibration Signal Features to Cutting Tool Wear in a Metal Turning Operation” International Journal of Advanced Manufacturing Technology, vol. 19, pp. 705-713, 2002.
  • [10] Choudhury, S. K., Bartarya, G., “Role of Temperature and Surface Finish in Predicting Tool Wear Using Neural Network and Design of Experiments” International Journal of Machine Tools & Manufacture., vol. 43, pp. 747-753, 2003.
  • [11] Mathew, P., “Use of Predicted Cutting Temperatures in Determining Tool Performance” International Journal of Machine Tools & Manufacture, vol. 29, pp. 481-497, 1989.
  • [12] Constantinides, N., Bennett, S., “An Investigation of Methods for the On-line Estimation of Tool Wear” International Journal of Machine Tools & Manufacture, vol. 27, pp. 225-237, 1987.
  • [13] Franco-Gasca, L. A., Herrera-Ruiz, G., Peniche-Vera, R., Romero-Troncoso, R. J., Leal-Tafolla, W., “Sensorless Tool Failure Monitoring System for Drilling Machines” International Journal of Machine Tools & Manufacture, vol. 46, pp. 381-386, 2006.
  • [14] Wang, Z. G., Lawrenz, W., Rao, R., Hope, A., “Feature-filtered Fuzzy Clustering for Condition Monitoring of Tool Wear” Journal of Intelligent Manufacturing, vol. 7, pp. 13-22, 1996.
  • [15] Fu, P., Hope, A., “Intelligent Classification of Cutting Tool Wear States” Lecture Notes in Computer Science (LNCS), vol. 3973, pp. 964-969, 2006.
  • [16] Vallejo, A. G., Nolazco-Flores, J. A., Morales-Menendez, R., Sucar, L. E., Rodriguez, C. A., “Tool-wear Monitoring Based on Continuous Hidden Markov Models” Lecture Notes in Computer Science (LNCS), vol. 3773, pp. 880-890, 2005.
  • [17] Ferraz, F., Coelho, R. T., “Data Acquisition and Monitoring in Machine Tools With CNC of Open Architecture Using Internet” International Journal of Advanced Manufacturing Technology, vol. 26, pp. 90-97, 2005.
  • [18] Hong, S. Y., “Knowledge-based Diagnosis of Drill Conditions” Journal of Intelligent Manufacturing, vol. 4, pp. 233-241, 1993.
  • [19] Du, R. X., Elbestawi, M. A., Li, S., “Tool Condition Monitoring in Turning Using Fuzzy Set Theory” International Journal of Machine Tools & Manufacture, vol. 32, pp. 781-796, 1992.
  • [20] Kaye, J. E., Yan, D. H., Popplewell, N., Balakrishnan, S., “Predicting Tool Flank Wear Using Spindle Speed Change” International Journal of Machine Tools & Manufacture, vol. 35, pp. 1309-1320, 1995.
  • [21] Fu, P., Hope, A., King, G. A., “A Neurofuzzy Pattern Recognition Algorithm and its Application in Tool Condition Monitoring Process” 4th International Conference on Signal Processing - ICSP, 1998.
  • [22] Amer, W., Ahsan, Q., Grosvenor, R. I., Prickett, P. W., “Machine Tool Condition Monitoring System Using Tooth Rotation Energy Estimation (TREE) Technique” 10th IEEE International Conference on Emerging Techonologies and Factory Automation - ETFA, 2005.
  • [23] Li, X., Tso, S. K., Wang, J., “Real-time Tool Condition Monitoring Using Wavelet Transforms and Fuzzy Techniques” IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews, vol. 30, pp. 352-357, 2000.
  • [24] Elbestawi, M. A., Dumitrescu, M., Ng, E. G., “Tool Condition Monitoring in Machining” Condition Monitoring and Control for Intelligent Manufacturing, Springer, London, United Kingdom, pp. 55-82, 2006.
  • [25] Li, G. S., Lau, W. S., Zhang, Y. Z., “In-process drill wear and breakage monitoring for a machine centre based on cutting force parameters” International Journal of Machine Tools & Manufacture, vol. 32, no. 6, pp. 855-867, 1992.
  • [26] Kanai, M., Kanda, Y., “Statistical Characteristics of Drill Wear and Drill Life for the Standardized Performance Tests” Ann. CIRP, vol. 27, pp. 61-66, 1978.
  • [27] El-Wardany, T. I., Gao, D., Elbestawi, M. A., “Tool Condition Monitoring in Drilling Using Vibration Signature Analysis” International Journal of Machine Tools & Manufacture, vol. 36, no. 6, pp. 687-711, 1996.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAR0-0068-0052
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