Production monitoring system design and implementation; pp. 10-16
Tanel Eiskop, A Snatkin, Kristo Karjust, E Tungel · Proceedings of the Estonian Academy of Sciences · 2017
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.