Selection of Optimum Features of Acoustic Emission Signal in Monitoring the Tool Wear of Milling Cutters

Develops a method for feature selection based on the differences in the measurement of separability of features according to the peculiarity of tool wear monitoring for small sized milling cutters. Experiments showed that characteristic vectors comprising the acoustic emission features optimized by this method can be effectively employed in the pattern recognition for tool wear monitoring.