A Neural Network Approach to On-line Monitoring of a Turning Process

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1992

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Production automation has been the focus of the research to improve product quality and to increase productivity. Implementation of computer-based untended machining has attracted great attention in the manufacturing community. In this paper, a framework for sensor-based intelligent decision-making systems to perform on-line monitoring is proposed. Such a monitoring system interprets the detected signals from the sensors, extracts the relevant information, and decide on the appropriate control action. Emphasis is given to applying neural networks to perform information processing, and to recognize the process abnormalities in a machining operation. A prototype monitoring system is implemented. For signal detection, an instrumented force transducer is designed and used in a real time turning operation. A neural network monitor based on a feedforward back- propagation algorithm is developed. The monitor is trained by the detected cutting force signal and measured surface finish. The superior learning and noise suppression abilities of the developed monitor enable high success rates for monitoring the cutting force and the quality of surface finish under the machining of advanced ceramic materials.

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