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A real-time drill monitoring algorithm is proposed, based on tool life and thrust models and real-time thrust measurements. The algorithm tracks the drilling process in two dimensions;drilling time and drilling thrust. A statistical approach wasdevelopedto consider the variation (both noiseand signal) present in the thrust measurements. The algorithm developed is based on an extensive drilling experiment involvingcarbon steel and four independent variables; cutting speed ranging from 32to 65sfpm(162to 330mms-'), feed rangingfrom0'0025to 0'015ipr(0'0635 to 0·381 mm), drill diameter ranging from 0·2969 to 0·5781 inch (7'54to 14·68 mm), and steel hardness ranging from 146 to 330BHN. A central composite designed experiment with 30 treatment combinations was used. The algorithm is demonstrated using data that were collected during the experiment. IntroductionA company's potential for growth is directly related to how effectively and efficiently the materials, manpower and equipment assigned to a process perform. Tooling is a critical part of many manufacturing processes. Tool management plays an important role in controlling tool performance and costs. Knowledge of tool behaviour during the cutting process and effective tool-failure prediction contribute to controlling machining costs by avoiding production delays and off-target parts, due to catastrophic tool failure as well as excessive tool wear.It is essential for a cutting tool to have a good cutting edge before it enters a cut. Once the tool has started cutting, it is desirable to monitor the cutting edge continuously to assure a fast corrective response if catastrophic tool failure occurs. If a broken tool is undetected during unmanned machining, considerable damage may be caused to the workpiece, the tool holder or the machine. When tool failures occur, it is necessary to act quickly in order to minimize damage. The appropriate action might be an emergency stop of the feed action or spindle rotation and/or fast retrieval of the cutting tool. It is obvious that an in-process tool-breakage sensing system is desirable, if not essential, for unmanned machining operations.Work has been performed in the area oftool monitoring by using cutting forces as predictors of tool behaviour. Langhammer (1976) studied the wear on carbide lathe tools and the machinability of steel by sensing the cutting forces. Radhakrishnan and Wu (1981) analysed the dynamic characteristics of drilling thrust and the corresponding hole surface using the dynamic data system (DDS) technique. Thangaraj and Wright (1988) performed a real-time control study of drilling by measuring the thrust force and its gradient. Smith (1989) suggested that a cutting tool can be monitored more effectively by continuously monitoring the thrust, rather than torque. Cutting force monitoring systems are based on the principle that an increase in the cutting force gives a good indication of the tool's condition as it approaches failure. Williams and

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