MENDED GENETIC BP NETWORK AND APPLICATION TO ROLLING FORCE PREDICTION OF 4-STAND TANDEM COLD STRIP MILL

In order to make good use of the ability to approach any function of BP (backpropagation) network and overcome its local astringency, and also make good use of the overallsearch ability of GA (genetic algorithms), a proposal to regulate the network's weights using bothGA and BP algorithms is suggested. An integrated network system of MGA (mended genetic algorithms)and BP algorithms has been established. The MGA-BP network's functions consist of optimizing GAperformance parameters, the network's structural parameters, performance parameters, and regulatingthe network's weights using both GA and BP algorithms. Rolling forces of 4-stand tandem cold stripmill are predicted by the MGA-BP network, and good results are obtained.

Prediction of Rolling Load in Hot Strip Mill byInnovations Feedback Neural Networks

Because the structure of the classical mathematical model of rolling load is simple, even with the self-adapting technology, it is difficult to accommodate the increasing dimensional accuracy. Motivated by this fact, an Innovations Feedback Neural Networks （IFNN） was presented based on the idea of Kalman prediction. The neural networks used the Back Propagation （BP） algorithm and applied it to the prediction of rolling load in hot strip mill. The theoretical results and the off-line simulation show that the prediction capability of IFNN is better than that of normal BP networks, namely, for the prediction of the rolling load in hot strip mill, the prediction precision of IFNN is higher than that of normal BP networks. Finally, a relative complete rolling load prediction system was developed on Windows 2003/XP platform using the OOP programming method and the SQL server2000 database. With this sys- tem, the rolling load of a 1700 strip mill was calculated, and the prediction results obtained correspond well with the field data. It shows that IFNN is valid for rolling load prediction.

Analysis of Hot Rolling Routes of AZ31B Magnesium Alloy and Prediction of Tensile Property of Hot-rolled Sheets

At the initial rolling temperature of 250 to 400 ℃, AZ31B magnesium alloy sheets were hot rolled by four different rolling routes. Microstructures and mechanical properties of the hot-rolled magnesium alloy sheets were analyzed by optical microscope and tensile tests respectively. Based on the Hall-Petch relation, considering the average grain size and grain size distribution, the nonlinear fitting analysis between the tensile strength and average grain size was carried on, and then the prediction model of tensile strength of hot-rolled AZ31B magnesium alloy sheet was established. The results indicate that, by rolling with multi-pass cross rolling, uniform, fine and equiaxial grain microstructures can be produced, the anisotropy of hot-rolled magnesium sheet can also be effectively weakened. Strong correlation was observed between the average grain size and tensile property of the hot-rolled magnesium alloy sheet. Grain size distribution coefficient d（CV） was introduced to reflect the dispersion degree about a set of grain size data, and then the Hall-Petch relation was perfected. Ultimately, the prediction accuracy of tensile strength of multi-pass hot-rolled AZ31B magnesium alloy was improved, and the prediction of tensile property can be performed by the model.

A Model to Predict Rolling Force of Finishing Stands with RBF Neural Networks

In view of intrinsic imperfection of traditional models of rolling force, in ord er to improve the prediction accuracy of rolling force, a new method combining radial basis function(RBF) neural networks with traditional models to predict rolling f orce was proposed. The off-line simulation indicates that the predicted results are much more accurate than that with traditional models.