Abstract
Abstract
Improvement of monitoring techniques is essential to make the complex dressing process more reliable, economical and user friendly. A system utilizing data from acoustic emission (AE) sensors and a suitable algorithm combined with a graphical user interface has been applied with this aim. The influence of different grinding wheel types and dressing parameters on the AE signal has been investigated and a dressing monitoring system is proposed. The root mean square (r.m.s.) signal, or the low-frequency component of the AE signal, provides information that can be utilized to monitor the process. A reliable touch detection and cycle termination can be established. The spectrum of the raw signal has been investigated for cubic boron nitride (CBN) and conventional grinding wheels. Reliable data acquisition techniques, which make a continuous scanning of such wide bandwidth signals possible, have been applied.