The prediction research of tool VB value based on Principal Component Analysis and SVR

DOI

10.2991/isrme-15.2015.11How to use a DOI?

Keywords

support vector regression(SVR);genetic algorithm; principal component analysis (PCA);forecast VB value

Abstract

According to the amount of tool wear prediction problems, online prediction of tool wear model is established based on the theory of support vector regression (SVR) regression. The acoustic emission signals and current signals are, respectively EEMD decomposedand wavelet packet decomposed to get the energy values, which are combined with the spindle speed, feeding, and back engagement to form the original feature vectors. By principalcomponent analysis for data processing, the principal elements as the Support Vector Regression (SVR) optimized by genetic algorithms are inputted. The results show that this model has high precision, fast operation.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Peng Nie
AU  - Chao He
AU  - Liang Xu
AU  - Kai-qi Cui
PY  - 2015/04
DA  - 2015/04
TI  - The prediction research of tool VB value based on Principal Component Analysis and SVR
BT  - Proceedings of the 2015 International Conference on Intelligent Systems Research and Mechatronics Engineering
PB  - Atlantis Press
SP  - 41
EP  - 44
SN  - 1951-6851
UR  - https://doi.org/10.2991/isrme-15.2015.11
DO  - 10.2991/isrme-15.2015.11
ID  - Nie2015/04
ER  -