A feature selection method for the milling force signal based on the improved Fruit Fly Optimization Algorithm

被引：0
|
作者：
Yuan M. [1 ]
Wang M. [1 ]
Pan Y. [1 ]
Hu M. [1 ]
机构：
[1] College of Manufacturing Science and Engineering, Sichuan University, Chengdu
来源：
Wang, Mei | 1600年 / Chinese Vibration Engineering Society卷 / 35期
关键词：
Feature selection; Fruit Fly Optimization Algorithm; Pattern recognition; Tool wear;
D O I：
10.13465/j.cnki.jvs.2016.24.031
中图分类号：
学科分类号：
摘要：
Feature selection is one of the key processes in pattern recognition. To solve the problem of identification of tool wear condition, a feature selection method based on the improved Fruit Fly Optimization Algorithm was proposed. Feature selection of cutting force was converted to food finding process of the fruit fly. The experiment was conducted on a Makino CNC milling machine equipped with: milling cutter, EGD440R; and insert material was A30N. Cutting force was extracted using Kistler 9257B three-phase dynamometer, analyzed by wavelet packet theory to reduce noise and extract the energy feature of the signal as a basis for feature selection. Then, an improved Fruit Fly Optimization Algorithm was established, in which Fisher discrimination was chosen as optimization criteria. The optimal feature subset was put into a BP neural network, which output the flank wear. The result of experiment indicates that the parameter of the model is easy to adjust, has good optimization result. As shown in Table1, the BP network performance has ample potential for cutting feature selection. © 2016, Editorial Office of Journal of Vibration and Shock. All right reserved.
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页码：196 / 200and206
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