Paper Title:
Surface Roughness Prediction of Engineering Ceramics Electro-Spark Machining Based on Rough Set Neural Network
  Abstract

To solve the problem of difficulty in establishing the mathematical model between process parameters and surface quality in the process of engineering ceramics electro-spark machining, a neural network relational model based on rough set theory is presented. By processing attribute reduction from data sample utilizing rough set theory, defects like bulkiness of neural network structure and difficult convergence etc are aovided when input dimensions is high. A prediction model that a surface roughness varies in accordance with processing parameters in application of well structured neural network rough set is established. Study result shows that utilizing this model can precisely predict surface roughness under the given conditions with little error which proves the reliability of this model.

  Info
Periodical
Edited by
Long Chen, Yongkang Zhang, Aixing Feng, Zhenying Xu, Boquan Li and Han Shen
Pages
269-273
DOI
10.4028/www.scientific.net/AMM.43.269
Citation
X. Li, H. Wang, S. F. Chen, "Surface Roughness Prediction of Engineering Ceramics Electro-Spark Machining Based on Rough Set Neural Network", Applied Mechanics and Materials, Vol. 43, pp. 269-273, 2011
Online since
December 2010
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: L.N. Hao, Wen Lin Chen, G.Y. Wu, Wan Shan Wang
783
Authors: L.J. Zhong, Ai Bing Yu, S.Y. Yu, Hai Yan Du
Abstract:A new method is proposed for machinability comprehensive evaluation of engineering ceramic materials based on digraph theory. Machinability...
256
Authors: Xiao Li Xu, Bin Ren, Yun Bo Zuo, Guo Xin Wu
Innovative Design Methodology
Abstract:In the high-end CNC machining process, the stability and reliability of the running state of the machining system directly affects the...
35
Authors: Bing Xiang Liu, Yan Wu, Meng Shan Li
Chapter 1: Digital Manufacturing and Advanced Manufacturing
Abstract:The decision tree is a widely used classification model and inductive learning method based on examples. It is characterized by the simple...
347
Authors: Bai Lin Liu, Li Xing Gao
Chapter 6: Measurement Techniques, Technologies and Equipment
Abstract:To solve the problem that large training samples and slow speed in diagnosing based on support vector classifier, a hybrid classification...
887