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.

A new groove design method of uniform strong strain of bar rolling was proposed based on the principle of strong plastic deformation to manufacture ultra-fine grain(UFG), and flat-oval groove type with the characteristics of big crushed and multidirectional deformation was set up. The numerical analysis model of hot continuous bar rolling process was created using nonlinear finite element method. The study of the law of plastic strain distribution after each rolling pass of the caliber series indicated that the caliber can satisfy with the precise size and shape, and can also better introduce the plastic strain to the center of cross section and get uniform strong strain. The largest strain was more than 5.0 in the center of cross section, in which condition ultra-fine grain can be fabricated. Therefore, this study provides important theoretical basis for the ultra-fine grain of bar rolling development.