The aim of the current study was to develop a highly flexible and scalable modular real-time monitoring system with predictive capabilities. The main focus was on forecasting tool/component life-span. A two-stage model is proposed for predicting tool life-span. Based on real-time monitoring data a back-propagation artificial neural network model was developed and validated. The obtained response surfaces for vibrations, current, and temperature are utilized in an analytical tool wear forecast model.
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