Prediction of processing effect of wire electrode discharge grinding of insulation engineering ceramic with particle swarm optimization fuzzy neural network

WANG He; YANG Yong

doi:10.13394/j.cnki.jgszz.2019.2.0017

Volume 39 Issue 2

Apr. 2019

Turn off MathJax

Article Contents

Article Navigation > Diamond & Abrasives Engineering > 2019 > 39(2): 89-94

WANG He, YANG Yong. Prediction of processing effect of wire electrode discharge grinding of insulation engineering ceramic with particle swarm optimization fuzzy neural network[J]. Diamond & Abrasives Engineering, 2019, 39(2): 89-94. doi: 10.13394/j.cnki.jgszz.2019.2.0017

Citation:

WANG He, YANG Yong. Prediction of processing effect of wire electrode discharge grinding of insulation engineering ceramic with particle swarm optimization fuzzy neural network[J]. Diamond & Abrasives Engineering, 2019, 39(2): 89-94. doi: 10.13394/j.cnki.jgszz.2019.2.0017

Citation:

WANG He, YANG Yong. Prediction of processing effect of wire electrode discharge grinding of insulation engineering ceramic with particle swarm optimization fuzzy neural network[J]. Diamond & Abrasives Engineering, 2019, 39(2): 89-94. doi: 10.13394/j.cnki.jgszz.2019.2.0017

PDF( 975 KB)

Prediction of processing effect of wire electrode discharge grinding of insulation engineering ceramic with particle swarm optimization fuzzy neural network

doi: 10.13394/j.cnki.jgszz.2019.2.0017

WANG He¹,
YANG Yong²

1. Mechanical Engineering Department, Henan University of Engineering, Zhengzhou 451191, China;
2. Shenyang Machine Tool(Dongguan) Intelligent Equipment Co., Ltd., Dongguan 523808, Guangdong, China

More Information

Rev Recd Date: 2019-03-13

Abstract

Abstract

In the process of wire electrode discharge grinding (WEDG) of insulating engineering ceramic, there is a close relationship between the technical indexes and process parameters. The operators can only set the process parameters according to their past experience during the practice, and predict the processing results to a certain extent. If the setting of process parameters is unreasonable, it will greatly affect the processing efficiency, accuracy and capacity of machine tools. Therefore, based on BP fuzzy neural network(BPFNN), a prediction model is presented for the effect of technical indicators for WEDG of insulating engineering ceramic. Rough set theory is used to reduce the attributes and rules of training samples and improved particle swarm optimization (PSO) is used to optimize the network. The models before and after optimization are used to simulate the processing of boron carbide (B₄C) ceramics and the results are compared. It is found that the optimized model has the advantages of fast prediction speed, small error and high precision.