A neural network approach to on-line monitoring of a turning process

A framework for sensor-based intelligent decision-making systems to perform online monitoring is proposed. Such a monitoring system interprets the detected signals from the sensors, extracts the relevant information, and decides on the appropriate control action. Emphasis is given to applying neural networks to perform information processing, and to recognizing the process abnormalities in machining operations. 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 backpropagation 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. >