A hybrid model using genetic algorithm and neural network for process parameters optimization in NC camshaft grinding

被引：10
|
作者：
Z H Deng
X H Zhang
W Liu
H Cao
机构：
[1] Hunan University,National Engineering Research Center for High Efficiency Grinding
[2] Huda Haijie Manufacture Technology Co.,undefined
[3] Ltd,undefined
来源：
The International Journal of Advanced Manufacturing Technology | 2009年 / 45卷
关键词：
Genetic algorithm; Neural network; Camshaft grinding; Uniform design; Process parameters optimization;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Camshaft grinding is more complex comparing with the ordinary cylindrical grinding. Since its quality is mostly influenced by more factors, how to select process parameters quickly and accurately becomes the key to improve its quality and processing efficiency. In this paper, a hybrid artificial neural network (ANN) and genetic algorithm (GA) model is proposed to optimize the process parameters. In this method, a BP neural network model is developed to map the complex nonlinear relationship between process parameters and processing requirements, and a GA is used in order to improve the accuracy and speed based on the ANN model. The results show that the hybrid ANN/GA model is an effective tool for the process parameters optimization in NC camshaft grinding.
引用
收藏
页码：859 / 866
页数：7
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