Selecting an artificial neural network for efficient modeling and accurate simulation of the milling process

被引：58

作者：

Bricenco, JF ^{[1
]}

El-Mounayri, H ^{[1
]}

Mukhopadhyay, S ^{[1
]}

机构：

[1] Indiana Univ Purdue Univ, Dept Engn Mech, Indianapolis, IN 46202 USA

来源：

INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE | 2002年 / 42卷 / 06期

关键词：

end millings; artificial neural networks; back propagation; radial basis;

D O I：

10.1016/S0890-6955(02)00008-1

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this paper. two supervised neural networks are used to estimate the forces developed during milling. These two Artificial Neural Networks (ANNs) are compared based on a cost function that relates the size of the training data to the accuracy of the model. Training experiments are screened based on design of experiments. Verification experiments are conducted to evaluate these two models. It is shown that the Radial Basis Network model is superior in this particular case. Orthogonal design and specifically equally spaced dimensioning showed to be a good way to select the training experiments. (C) 2002 Elsevier Science Ltd. All rights reserved.

引用

页码：663 / 674

页数：12