Reversible Cold Rolling Process Time Optimization for an Industrial Application

Reversible cold milling is a widely used industrial process for metal forming. It consists of backward and forward motion of the sheet metals between the rolling mills. As the process is highly automated, optimization of the process parameters is essential for cost minimization. However, it is a complex optimization problem because maximization of the rolling speed increases the motor mill currents and rolling force, and physical constraints of the line may not allow to achieve that. In this paper regression and artificial neural network models are developed to predict process parameters. Genetic algorithm with the fitness function based on regression model is used to optimize total time of process. Performance of both prediction method and genetic algorithm are validated with past industrial data and field experiments. The results show that the developed method is an effective tool for the process optimization of reversible cold rolling process.