The objective of this paper is to construct an automatic monitoring system for online detection of tool breakage in milling processes, which always occurs unexpectedly and causes a catastrophe to the workpiece or even the machine tool. Since none of sensing and diagnosis techniques have proven to be completely reliable during practical operations, an intelligent tool monitoring system consisting of a neural-network-based algorithm and a sensor fusion system is proposed. The dual sensing signals of cutting force and acoustic emission are simultaneously used in the proposed system due to a good correlation existing between them. And, the neural-network-based algorithm developed by Lou and Wu (1994) is used to integrate multiple sensing information. In this paper, a simulation approach is adopted which means analytical models are used to generate the simulated signals instead of real measurements. Then finally, a variety of simulation examples are presented to demonstrate the efficiency of the proposed system.
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