神经网络算法与机理模型融合的冷连轧轧制力智能预报模型  

Intelligent Prediction Model Based on Neural Network Algorithm and Mechanism Model for Rolling Force in Tandem Cold Rolling

作　　者：崔晨耕[1] CUI Chen-geng(School of Electronic Engineering, Xi’an Aeronautical Polytechnic Institute, Xi’an 710089, China)

机构地区：[1]西安航空职业技术学院电子工程学院

出　　处：《计算机与现代化》2019年第8期74-78,91,共6页Computer and Modernization

基　　金：全国住房和城乡建设职业教育教学指导委员会科研计划项目(zjzw2016072)

摘　　要：冷轧带钢的生产模式正在向多品种、小批量、低库存转变,这直接导致了轧制过程中需要更频繁地切换品种规格。由于变规格过程中轧制工艺状态变化较大,传统机理模型加常规自学习和自适应方法很难保证规格切换后首卷产品设定精度。为提高变规格过程中轧制力预报精度,本文提出一种机理模型与轧制过程海量历史数据相融合的复合模型。该方法建立在轧制力理论模型的基础上,采用遗传算法优化BP神经网络的方法对模型轧制力进行校正。使用该算法进行轧制力预报,使变规格后首卷钢轧制力预报的平均相对误差控制在±5.5%内,远高于常规机理模型的设定精度,该方法具有现场应用价值。The production mode of cold rolled strip is changing to multivarieties, small batch and low inventory, which directly results in the need to change the varieties specifications more frequently during rolling. Because changes in product specifications and rolling process status are large, the traditional mechanism model and conventional self-learning and adaptive method are difficult to ensure the setting accuracy of the first volume product. In order to improve the prediction accuracy of rolling force in the process of variable specification, this paper puts forward a composite model based on mechanism model and mass history data of rolling process. The new rolling force model, based on the theoretical model of rolling force, is corrected by BP neural network optimized by genetic algorithm (GA). The new method for prediction of the rolling force makes the average relative error of the first specification of rolled steel rolling force prediction be controlled within 5.5%. The setting accuracy is much higher than that of the conventional mechanism model.

关 键 词：定制化生产 轧制力预报 遗传算法 BP神经网络 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]