Cold rolling force model of nuclear power zirconium alloy based on Particle Swarm Optimization

The rolling force model is the core of rolling model, and the prediction accuracy of the Zr-4 alloy cold rolling force model directly affects the control accuracy of the thickness and shape of the Zr-4 alloy strip. In this paper, the cold rolling force model of the nuclear power Zr-4 alloy is studied based on the adaptive Particle Swarm Optimization (PSO) algorithm. The cold rolling deformation resistance model of the nuclear power Zr-4 alloy was obtained by field actual sampling and tensile test. The friction coefficient model of the nuclear power Zr-4 alloy was studied based on the Particle Swarm Optimization (PSO) algorithm according to the field measured rolling force value. According to the Hill model, a simplified method of external friction stress state coefficient model based on global variables is proposed by using the Particle Swarm Optimization (PSO) algorithm. Finally, the cold rolling force model of the Zr-4 alloy is established. Industrial test proves that the average cold rolling force prediction accuracy of the nuclear power Zr-4 alloy is 95.36%, which can effectively guide the rolling control of the nuclear power Zr-4 alloy cold rolling production process.

Automated summary

This study focuses on the cold rolling force model of nuclear power Zr-4 alloy, obtaining the cold rolling deformation resistance model and friction coefficient model using the PSO algorithm. A simplified method of the external friction stress state coefficient model based on global variables is proposed. Industrial tests showed an average prediction accuracy of 95.36%, effectively guiding the rolling control of the cold rolling production process.