By E.H. Dowell
Publication by means of Dowell, E.H.
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377–380. , Z. J. Pasek, Y. Shan, Y. Koren, K. G. Shin, and A. G. Ulsoy, 1996, “An Open-Architecture Real-Time Controller for Machining Processes,” Manufacturing Systems, Vol. 25, No. 1, pp. 23–27. Schofield, S. and P. Wright, 1998, “Open Architecture Controllers for Machine Tools, Part 1: Design Principles,” ASME J. Manufacturing Science and Engineering, Vol. 120, No. 2, May, pp. 417–424. , 1986, “Dynamics of High Speed Machining,” ASME J. of Engineering for Industry, Vol. 108, pp. 59–67. Koren, Y.
58]. Generally, a neural network is trained to identify the tool wear pattern by supervised learning from samples of measurements taken at various levels of tool wear. Therefore, the ability of neural networks to form reliable wear patterns depends not only on their topology, but the extent of their training. In cases such as machining, where adequate data is not available to select the topology of the network, or to provide the tool wear patterns for a wide range of cutting conditions and material/tool combinations, these networks are not practical.