Optimization Model of the Cold Rolling Roll Grinding Volume Based on Nonlinear Fatigue Accumulation Regulation

Abstract

The main form of roll failure in cold rolling production is fatigue damage. To avoid industrial production accidents, the roll grinding amount after service is usually manually determined in accordance with empirical settings. This leads to excessive grinding. To address this problem, this work takes a 2130-mm UCM cold rolling production line as an example and establishes an elastic deformation model of the mill roll system based on the influence function method. The three-way stress and maximum tangential stress distribution inside the roll are calculated by means of the Hertz formula. Combined with the nonlinear fatigue accumulation model, a roll fatigue prediction model is proposed to quantitatively describe the roll fatigue state. According to the roll fatigue distribution data and actual production situation, the roll fatigue curve is shifted superimposed, and the roll grinding optimization model is established using a genetic particle swarm algorithm. It can meet the requirements of industrial production in terms of convergence speed and optimization. It applies the model to a cold rolling production line in a factory. Field application shows that the model can be used to quantify and detect roll fatigue in real time. The rolls can be dismounted at the right time, and the production accident rate from fatigue can be reduced by 33.3%. The consumption of backup rolls, intermediate rolls and work rolls can be reduced by 23.2, 24.1 and 28.6% each year, respectively. The application of this model can greatly reduce the production cost for a plant, ensure production safety and extend the service life of rolls.