A Hybrid Model Using Genetic Algorithm and Neural Network for Optimizing Technology Parameters in Skew Rolling Seamless Pipes

摘要：

In the production of seamless pipes, there are many factors influencing the quality of the seamless tubes such as diameter of original pipe, feed angle, temperature, minimum roll gap and so on. Unfortunately, the selection of the parameters still relies on manual operation and the experience of engineers. In this paper, a novel hybrid model through integration of genetic algorithm(GA) and neural network is proposed to optimize the technology parameters. Firstly, the neural network model is developed between technology parameters and final pipes dimensions. Then, genetic algorithm is used to optimize the neural network structure. At last, accuracy of the parameters is verified by experiment. The research results indicate that the proposed approach can effectively help engineers determine optimal technology parameters and achieve competitive advantages of product quality and costs.

关键词： Neural network GA Skew rolling Seamless pipe

作者: Jian-hua Hu Yuan-hua Shuang Peng Liu

作者单位: Department of Material Science and Engineering, Taiyuan University of Science &Technology，Taiyuan 030024, Shan Xi, China

会议类型: 国际会议

会议名称: International Symposium on Advanced Rolling Equipment Technologies(第一届轧钢设备新技术国际研讨会 ISARET 2010)

会议地点: 太原

会议语种:英文

页码: 117-122

在线出版日期: 2010-09-23（万方平台首次上网日期，不代表论文的发表时间）