The application of a radial basis function neural network for predicting the surface roughness in a turning process

被引：0
|
作者：
D.K. Sonar
U.S. Dixit
D.K. Ojha
机构：
[1] Indian Institute of Technology,Department of Mechanical Engineering
来源：
The International Journal of Advanced Manufacturing Technology | 2006年 / 27卷
关键词：
Radial basis function neural networks; Surface roughness; Dry and wet turning ;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This study considers the performance of a radial basis function neural network for predicting the surface roughness in a turning process. A simple algorithm is proposed for finding the upper and lower estimates of the surface roughness. A code is developed that automatically fits the best network architecture for a given training and testing dataset. The validation of the methodology is carried out for dry and wet turning of mild steel using HSS and carbide tools, and is compared to the performance of the studied network with the reported performance of a multi-layer perception neural network. It is observed that the performance of the radial basis function network is slightly inferior compared to multi-layer perceptron neural network. However, the training procedure is simpler and requires less computational time.
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页码：661 / 666
页数：5
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