Chaotic characteristics extracting and trend analysis on acoustic emission signal of tool condition

This paper is aim to build a monitoring method based on several chaotic characteristics,through chaotic characteristics analyzing of the acoustic emission signal from cutting tool and analyzing the trends of tool wear.Chaotic characteristics analyzed and described based on chaotic theory in different periods of acoustic emission signal from cutting tool,are including:(1) qualitative description,reconstructing the strange attractor track and Poincare map of the acoustic emission series;(2) quantitative description,computing correlation dimension and the max lyapunov exponent of the acoustic emission signal at different periods.Then chaotic characteristics computing result were analyzed by the least square method.The results show that chaotic phenomena exist in acoustic emission signal,and the correlation dimension and the max Lyapunov exponent have relationship with tool wear state.At last the paper provides a new idea for online monitoring and prediction of tool wear state.