Optimal Design Of Hot Rolling Process Based On Control Theory

The design of TMCP (Thermal Mechanical Controlled Processing) parameters for hot rolled plate/strip is a time-consuming and exhausting research work. At the same time, because of the complexity of microstrual evolution and constraints of equipments in hot rolling process, it is difficult to determine the optimal TMCP parameters in traditional way. Therefore, development of new ways, which is to optimize the hot rolling process with the mechanical properties set up as the target, is of great necessity. The mathematical model for description of microstructure evolution and mechanical properties prediction, and the optimization of hot rolling process under the constraints of equipments to achieve the required mechanical properties constitute the basic concept of optimal design of hot rolling process. In the present paper, the physical-metallurgical theory and the artificial intelligence were applied in the modeling work for the microstructural evolution and the relationship between chemical compositions, process parameters and mechanical properties, accounting for the regularity and prediction precision. The optimal design of hot rolling process was finally realized by the employment of single-and multi-objective particle swarm optimization method. The chief original work of this paper is as follows.(1) Research work concerning the effectiveness of additivity rule applied to the modeling of phase transformation in the continuous cooling processThe dilatometric test was employed to measure the transformation kinetics of austenite to ferrite in four kinds of Nb bearing steels with different niobium contents. The theoretical method developed by Rios was for the first time applied to the modeling of phase transformation in the continuous cooling process. The relationship between lnln[1/(1-X)] and ln(|CR|) (CR-cooling rate) was plotted using the data at early stage of transformation, which successfully fitted a linear relationship to calculate the exponential values of n in the JMAK equation. The values of k in the JMAK equation obtained with the Rios method, however, have led to big discrepancies when the isothermal equations were used to predict the transformation kinetics during cooling. By assuming a Gaussian dependence of temperature, k was calculated by using an optimization method based on the rule of additivity and exhibited cooling rate correlation. The isothermal transformation model was used to predict the transformation kinetics during cooling, showing good agreements with the measured data. It has been proved that even though the rule of additivity has to be relaxed to take into account the effects of cooling rates, precise conversion between non-isothermal and isothermal kinetics can still be realized.(2) Modeling of phase transformation during continuous cooling process based on mathematical anlysisBased on the Rios'method, a new mathematical method designed to analyze the dilation data of phase transformation in the continuous cooling process was developed. The modeling work of phase transformation kinetics was solved mathematically. According to the preset of exponential n and temperature step, the parameter k at any moment during the transformation was calculated based on the inverse application of additivity rule. Relationship between k and temperature and transformed fraction dependence of k could be revealed by the comparison of k under different cooling rate. Combined with the Rios'method, the transformation model for TRIP and CP steels were developed. Good agreements between measured and predicted value of transformation kinetics have validated the effectiveness of the new method.(3) Application of Bayesian neural network in the microstructure and mechanical properties prediction for the hot rolling processThe punishment item, based on the "Occam's razor" theory, was introduced into the objective function for the training of neural network in order to prevent the occurrence of "over-fitting" and improve the generalization ability. Extraodinary results have been obtained when the Bayesian neural network was applied to the modeling work in the field of hot torsion, welding and fatigue crack growth rate by Bhadeshia and Mackey. In the present work, the Bayesian neural network was programmed and used to model the relationship between chemical composition, process parameters and mechanical properties, wich has exhibited better stability, fast convergence rate and generalization ability when compared with the traditional BP neural network. It could provide model basis with high precision for the optimal design of hot rolling process.(4) The realization of optimal design of hot rolling processCombined with the single-objective particle swarm optimization method, the phase transformation model was used to realize the optimal design of continuous cooling process. Different ferrite grain size and combination of ferrite, bainite and martensite phases were obtained according to the control of cooling rate. And based on the Bayesian neural network, the multi-objective particle swarm optimization method was emplyed to build up the optimal design system for the whole hot rolling process. The process window was determined through the calculation of Pareto front with a high precision, which could be difficult for single-objective optimization. The production data has suggested that, the required mechanical properties can be achieved through the optimization of process parameters based on the chemical composition and constraints of equipment. It could provid theoretical guidance and technical support for flexible production mode.