A framework for intelligent sensors in unmanned machining is proposed. In the absence of human operators, the process monitoring function has to be performed with sensors and associated decision-making systems which are able to interpret incoming sensor information and decide on the appropriate control action. In this paper, neural networks are used to integrate information from multiple sensors (acoustic emission and force) in order to recognize the occurrence of tool wear in a turning operation. The superior learning and noise suppression abilities of these networks enable high success rates for recognizing tool wear under a range of machining conditions. The parallel computation ability of these networks offers the potential for constructing intelligent sensor systems that are able to learn, perform sensor fusion, recognize process abnormalities, and initiate control actions in real-time manufacturing environments.

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