Abstract
Combined with the research status of springback of sheet metal form in drawing, an neural network modeling method based on improved particle swarm optimization (IPSO) algorithm is proposed to solve the problem that tradition neural network optimal algorithm is easy to fall into local searching. In order to avoid the particles falling into local searching and improve the particles convergence rate, a new the strategy is put forward to update the particle's velocity and location. The training samples of the neural network are obtained by numerical simulation, and then prediction model of ANN is established based on neural network, of which the process parameters are input and resilience value is output. On this basis, process parameters of sheet metal forming in drawing process are optimized using IPSO. The results show that IPSO can not only improve springback prediction accuracy, but also provide a new research approach to the process parameters of sheet metal forming in drawing.
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.
