Optimum Design of Rolling Schedule for Tandem Cold Mill Using SLPSO

Abstract

This paper proposes a new method to optimize cold strip rolling schedule by means of self-adaptive learning based particle swarm optimization (SLPSO). Multiple strategies may be adopted based on their previous behaviors in the searching. This particle swarm optimization version is robust and effective in solving complex problems. Function of power cost was constructed to heuristically direct the SLPSO searching, based on the consideration of power distribution, speed and rolling constraints. The results of simulation demonstrate that SLPSO is more efficient in calculating than others, and provides a new valid method for the intelligent optimum design of scheduling tandem cold strip mill.

Rolling Schedule Multi-objective Optimizationbased on Influence Function for Thin Gauge Steel Strip in Tandem Cold Rolling

“…The products with higher dimensional accuracy and better mechanical property have been produced. Among them, cold rolled sheet has been applied to electronics, automobile, light industry, textile, and other sectors widely due to its characteristics like good atness, bright surface, and uniform performance [9][10][11][12][13][14]. However, with the explosive development of modern industrial technology and automated production processes, sectors of the national economy feel the increasingly widespread need for rolled products with higher precision of dimensions.…”