Abstract
In this paper, an approach is proposed to apply parallel decision-making systems on the same target, and to identify the cutting state synthetically based on evaluation of the results obtained by the plural subsystems. In the present work, a system was developed having two subsystems to estimate chipping of cutting edges during end milling. The subsystems employ artificial neural networks based on the cutting force signals. The chipping of the cutting edge was well identified for different cases of end milling. It is concluded that the flexibility and the reliability of the monitoring system is much improved by operating the subsystems in parallel as proposed in the present study.
