MATEC Web Conf.
Volume 82, 20162016 International Conference on Design, Mechanical and Material Engineering (D2ME 2016)
|Number of page(s)||7|
|Section||Chapter 2: Materials Engineering|
|Published online||31 October 2016|
Chaotic Characteristics and the Application of SVM in the Tool Wear State Recognition
Northeast Dianli University school of mechanical engineering, Jilin, China, 132012
a Corresponding author: email@example.com
Metal cutting process is a nonlinear system to obtain the tool wear state and chaos theory are introduced tool wear and feature extraction of acoustic emission signal analysis and classification of tool wear state and wear prediction based on support vector machine (SVM). First, optimal embedding dimension of the time delay of phase space reconstruction of nonlinear dynamic system, the chaotic attractor; secondly, three characteristics: correlation dimension, the largest Lyapunov exponent and the Kolmogorov is extracted from the AE signal denoising feature vector and construct the different wear conditions. Finally, the feature vector is fed into the support vector machine (SVM), and the tool wear condition is classified. Research shows that: the cutting tool wear acoustic emission signal possesses the characteristics of chaos, chaotic characteristic parameters and tool wear status has intrinsic relationship; combined with chaos theory and support vector machine (SVM), can be very good to achieve the tool wear state recognition and prediction.
© The Authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.