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High Speed Machining (HSM) has become one of the leading methods in the improvement of machining productivity. The term HSM covers high spindle speeds, high feed rates, as well as high acceleration and deceleration rates. Furthermore, HSM does not imply only working with high speeds but also with high levels of precision and accuracy. Additional to the HSM, many companies producing machine tools are interested in new technologies which provide intelligent features. Several research works (Koren et al., 1999; Erol et al., 2000; Liang et al., 2004) predict that future manufacturing systems will have intelligent functions to enhance their own processes, and the ability to perform an effective, reliable, and superior manufacturing procedures. In the areas of process monitoring and control, these new systems will also have a higher process technology level. In any typical metal-cutting process, the key indexes which define the product quality are dimensional accuracy and surface roughness; both directly influenced by the cutting tool condition. One of the main goals in a Computer Numerically Controlled (CNC) machining centre is to find an appropriate trade-off among cutting tool condition, surface quality and productivity. A cutting tool condition monitoring system which optimizes the operating cost with the same quality of the product would be widely appreciated, (Saglam & Unuvar, 2003; Haber & Alique, 2003). For example, in (Tonshoff et al., 1988), it has been demonstrated that effective machining time of the CNC milling centre could be increased from 10 to 65% with a monitoring and control system. Also, (Sick, 2002) mentions that any manufacturing process can be significantly optimized using a reliable and flexible tool monitoring system. The system must develop the following tasks: • Collisions detection as fast as possible. • Tool fracture identification. • Estimation or classification of tool wear caused by abrasion or other influences. While collision and tool fracture are sudden and mostly unexpected events that require reactions in real-time, the development of wear is a slow procedure. This section focuses on O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m

On-line Cutting Tool Condition Monitoring in Machining Processes Using Artificial Intelligence