摘要
针对基于层别数据的传统轧制模型自学习方法导致相邻层别上自学习系数跳跃大、不连续等问题,提出"机理模型+特征点+拟插值+自学习"的轧制模型构建新机制,用多维空间连续曲面代替原来的层别,实现轧制模型结构的升级。构造用特征点表征的连续曲面,采用连续函数对空间中各个特征点上的自学习系数进行拟插值的方法,获得光滑曲面方程。从相邻层别不连续变为多维空间上连续可导,使模型自学习系数精确到空间任意点,对提升模型设定精度有质的突破。该模型自学习方法目前已成功应用于国内某大型热连轧机组变形抗力在线计算,实际生产应用表明,新方法上线后变形抗力与轧制力的预报精度显著提高,带钢因厚度超差导致的预封锁量减少了44%,满足热连轧带钢稳定轧制的生产要求。
The traditional self-learning method of rolling model based on division of layer,resulting in the problems of model self-learning coefficients of adjacent layers are jumping greatly,discontinuous and other issues,the construction mechanism of rolling model based on ＂mechanism model＋feature points＋quasi-interpolation＋self-adaptive＂ is proposed,which replacing the original layer concept with multidimensional space continuous surface and upgrading the structure of rolling model. Constructing continuous surfaces characterized by feature points and the continuous function is used to interpolate the self-learning coefficients of each feature point in space to obtain the equation of smooth surface. The multidimensional space are continuous and differentiable adjacent layers,so the model self-learning coefficients can correct to any point in the space,which make a great breakthrough with the precision of the rolling model. The model has been successfully applied to on-line calculation of deformation resistance in a large hot strip mill in China. The practical application shows that the prediction accuracy of deformation resistance and rolling force is improved remarkably after the new method on line,the pre-blockade capacity of strip steel due to oversize thickness reduced by 44% to meet the hot strip rolling strip production requirements.
出处
《钢铁》
CAS
CSCD
北大核心
2017年第12期61-66,共6页
Iron and Steel
基金
国家自然科学基金资助项目(51774219)
湖北省教育厅科学技术研究资助项目(D20161103)
武汉市青年科技晨光计划资助项目(2016070204010099)
东北大学轧制技术及连轧自动化国家重点实验室开放课题基金资助项目(2017RALKFKT004)
关键词
板带轧制
数学模型
自学习
连续曲面
拟插值
strip rolling
mathematical model
self-learning
continuous surface
quasi-interpolation
