Strip thickness prediction based on random forest feature selection and genetic algorithm optimization
In tandem cold rolling,strip thickness accuracy is one of the most important indicators to evaluate product quality and that downstream enterprises are concerned about.The existing methods for strip thickness prediction mainly focus on the hit rate of the strip head thickness,which cannot reflect the thickness accuracy and fluctuation of the whole coil.A continuous prediction model based on deep learning algorithm for the full length thickness of strip in tandem cold rolling is proposed,and the mapping from industrial data to strip thickness is realized.The data set is built by actual data of a five-stand tandem cold rolling produc-tion line.Random forest is used for feature selection to simplify the input features,and then genetic algorithm is used to optimize the initial weights and thresholds of DNN model for the further model performance improvement.The results show that the model can accurately and intuitively reflect the thickness accuracy and fluctuation of the whole coil with rolling time.In accelera-tion,deceleration and stable rolling stage,the relative errors of predicted results are controlled within+0.5％and±0.1％,which meet the actual production requirements.By using this model,the control system can improve the pre-setting accuracy and implement pre-adjustment at the corresponding time,so as to achieve the purpose of strip thickness accuracy improving.
tandem cold rollingstrip thickness accuracyfull length thickness of strippredictionrandom forestfeature selectiondeep neural networkgenetic algorithm
NETL
NSTL
万方数据
