Abstract
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
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
