期刊
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY
卷 205, 期 1-3, 页码 16-23出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.jmatprotec.2007.11.082
关键词
free machining steel; turning; surface roughness; orthogonal array; artificial neural network
Surface roughness prediction models using artificial neural network (ANN) are developed to investigate the effects of cutting conditions during turning of free machining steel, 9SMnPb28k(DIN). The ANN model of surface roughness parameters (R-a and R-t) is developed with the cutting conditions such as feed rate, cutting speed and depth of cut as the affecting process parameters. The experiments are planned as per L-27 orthogonal array with three levels defined for each of the factors in order to develop the knowledge base for ANN training using error back-propagation training algorithm (EBPTA). 3D surface plots are generated using ANN model to study the interaction effects of cutting conditions on surface roughness parameters. The analysis reveals that cutting speed and feed rate have significant effects in reducing the surface roughness, while the depth of cut has the least effect. The details of experimentation, ANN training and validation are presented in the paper. (c) 2007 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据