One of the ways of attaining higher productivity and profitability in machining processes is to enhance process supervision and control systems. Because of the nonlinear behavior and complexity of machining processes, researchers have used knowledge-based techniques to improve the performance of such systems. Their main reason for using this approach is that a suitable process model is indispensable for both automatic supervision and control, yet traditional approaches frequently fail to yield appropriate models of complex (nonlinear, time-varying, ill-defined) processes, such as machining certainly is, while knowledge-based methods provide novel tools for dealing with process complexity. One of the most powerful of these tools is fuzzy logic, which was the authors ’ chosen design approach. An overview is given of the main aspects of fuzzy logic and its application to modeling and control by means of the so-called Fuzzy Logic Device (FLD). Available methods suitable for process supervision are also reviewed, including pattern recognition and so-called intelligent supervision. Emphasis is placed on modeling by means of fuzzy clustering techniques. The machining process is typified with a systemic (input/output) approach, as is necessary for modeling and control purposes. Finally the
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