Flank wear estimation in face milling based on radial basis function neural networks

被引：23

作者：

Pai, PS

Nagabhushana, TN ^{[1
]}

Rao, PKR

机构：

[1] Sri Jayachamarajendra Coll, Dept Comp Sci & Engn, Mysore 570006, Karnataka, India

[2] Sri Jayachamarajendra Coll, Dept Engn Mech, Mysore 570006, Karnataka, India

来源：

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY | 2002年 / 20卷 / 04期

关键词：

batch fuzzy C means; flank wear; radial basis function; resource allocation network;

D O I：

10.1007/s001700200148

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents an estimation of flank wear in face milling operations using radial basis function (RBF) networks. Various signals such as acoustic emission (AE), surface roughness, and cutting conditions (cutting speed and feed) have been used to estimate the flank wear. The hidden layer RBF units have been fixed randomly from the input data and using batch fuzzy C means algorithm, and a comparative study has been carried out. The results obtained from a fixed RBF network have been compared with those from a resource allocation network (RAN).

引用

页码：241 / 247

页数：7

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