Study on the method of cold rolling force optimization based on data mining
The deformation resistance and friction parameters in the widely used Bland-Ford-Hill model of cold rolling force technology are optimized,by mining the hidden rules within the actual data,to improve the calculation accuracy of rolling force.In the beginning,the inverse algorithm of deformation resistance and the friction calculation was derived,and the optimized regression deformation resistance model parameters and friction coefficient model parameters were calculated by using L-M nonlinear polynomial regression method to establish the rolling force optimization model.Then,according to the massive actual field data and using data mining method,the model parameters which suit deformation resistance and friction coefficient better were obtained by adopting the above optimization method.After running the optimization results online,the rolling force accuracy is improved obviously.
cold rolling force optimizationdeformation resistancefriction coefficientinverse calculationL-M nonlinear polynomial regressiondata mining
NETL
NSTL
万方数据
维普
