Artificial Neural Networks for Controlling the Temperature of Internally Cooled Turning Tools
- 1 UPM Ltd., Swindon, England
- 2 Brunel University, London, England
- 3 Brunel University, London, England
- 4 Technische Universit?t, Berlin, Germany
- 5 Technische Universit?t, Berlin, Germany
- 6 Technische Universit?t, Berlin, Germany
- 7 Technische Universit?t, Berlin, Germany
Abstract
By eliminating the need for externally applied coolant, internally cooled turning tools offer potential health, safety and cost benefits in many types of machining operation. As coolant flow is completely controlled, tool temperature mea surement becomes a practical proposition and can be used to find and maintain the optimum machining conditions. This also requires an intelligent control system in the sense that it must be adaptable to different tool designs, work piece materials and machining conditions. In this paper, artificial neural networks (ANN) are assessed for their suitability to perform such a control function. Experimental data for both conventional tools used for dry machining and internally cooled tools is obtained and used to optimise the design of an ANN. A key finding is that both experimental scatter characteristic of turning and the range of machining conditions for which ANN control is required have a large effect on the optimum ANN design and the amount of data needed for its training. In this investigation, predictions of tool tem perature with an optimised ANN were found to be within 5°C of measured values for operating temperatures of up to 258°C. It is therefore concluded that ANN’s are a viable option for in-process control of turning processes using inter nally controlled tools.
- E. Uhlmann and M. Roeder, “Internal Cooling of Cutting Tools,” Lambda Map Conference, London, July 2009.
- F. Klocke, “Dry Cutting, Keynote Paper,” Annals of the CIRP, Vol. 46, No. 2, 1997, pp. 519-526.
- V. P. Astakhov, “Effects of the Cutting Feed, Depth of Cut, and Workpiece (Bore) Diameter on the Tool Wear Rate,” International Journal of Advanced Manufacturing Technology, Vol. 34, No. 7-8, 2007, pp. 631-640.
- E. Dimla Jr., P. E. Lister and N. J. Leighton, “Neural Network Solutions to the Tool Condition Monitoring Problem in Metal Cutting—A Critical Review of Methods,” International Journal of Machine Tools and Manufacture, Vol. 37, No. 9, 1997, pp. 1291-1241.
- D. A. Dornfield, “Neural Network Sensor Fusion for Tool Condition Monitoring,” Annals CIRP, Vol. 39, No. 1, 1990, pp. 101-105. doi:10.1016/S0007-8506(07)61012-9
- T. I. Liu and E. J. Ko, “On-Line Recognition of Drill Wear via Artificial Neural Networks,” Winter Annual Meeting of the ASME, Monitoring and Control for Manufacturing Processes, Vol. 44, Dallas, 1990, p. 101.
- S. Purushothaman and Y. G. Srinivasa, “A Back Propogation Algorithm Applied to Tool Wear Monitoring,” International Journal of Machine Tools and Manufacture, Vol. 34, No. 5, 1994, pp. 625-631.
- Q. Liu and Y. Altintas “On-Line Monitoring of Flank Wear in Turning with Multilayered Feed-Forward Neural Networks,” International Journal of Machine Tools and Manufacture, Vol. 39, No. 12, 1994, pp. 1945-1959. doi:10.1016/S0890-6955(99)00020-6
- L. I. Burke and S. Rangwala, “Tool Condition Monitoring in Metal Cutting: A Neural Network Approach,” Journal of Intelligent Manufacturing, Vol. 2, No. 5, 1991, pp. 269-280. doi:10.1007/BF01471175
- L. I. Burke, “Competetive Learning Approaches to Tool Wear Identification,” IEEE Transactions on System, Man and Cybernetics, Vol. 22, No. 3, 1992, pp. 559-563.
- A. Ruiz, D. Guinea, J. Barrios and F. Betancourt, “An Empirical Multi-Sensor Estimation of Tool Wear,” Mechanical Systems and Signal Processing, Vol. 7, No. 2, 1993, pp. 105-119.
- Q. Zhou, G. S. Hong and M. Rahman, “A New Tool Wear Criterion for Tool Condition Monitoring using Neural Networks,” Engineering Applications of Artificial Intelligence, Vol. 8, No. 5, 1995, pp. 579-588. doi:10.1016/0952-1976(95)00031-U
- Q. Zhou, G. S. Hong and M. Rahman, “On-Line Cutting State Recognition in Turning Using a Neural Network,” International Journal of Advanced Manufacturing Technology, Vol. 10, No. 2, 1995, pp. 87-92. doi:10.1007/BF01179276