摘要
基于非均匀热流边界条件,采用热流固耦合方法,研究了喷嘴长度与宽度、合金液喷射速度、冷却辊厚度和转速对冷却辊热变形的影响。在此基础上,建立了基于广义回归神经网络(GRNN)的冷却辊热变形预测模型,进一步提出了基于该预测模型的恒间距控制方法。结果表明,相比冷却辊转速和厚度,合金液参数如喷嘴长度、宽度与喷射速度对冷却辊变形的影响更为明显;基于GRNN的神经网络预测模型的精度较高,平均相对误差为5.98%;与传统PID控制方法相比,本文提出的间距控制策略可实现喷嘴包快速、精确地跟踪冷却辊变形,保证了间距的恒定,显著提高薄带初始制备过程中的合格率和生产效率。
The effects of nozzle length and width,alloy injecting speed,as well as roller thickness and speed on roller thermal deformation were investigated by thermal-structure coupling method based on the non-uniform heat flow boundary.Furthermore,a neural network prediction model of roller thermal deformation based on generalized regression neural networks(GRNN)was constructed,and a new control strategy of the constant gap based on the prediction model was proposed.The results show that the influence of nozzle width and length on roller deformation is more obvious than that of roller speed and thickness;the accuracy of the proposed neural network prediction model based on GRNN is acceptable,and the average relative error is 5.98%;compared with the traditional PID control method,the proposed control strategy of the gap has no lag and can meet the requirements of fast and accurate tracking of cooling roller deformation,ensuring a constant gap and significantly improving the qualification rate and production efficiency during the initial preparation of thin ribbons.
作者
姜海蛟
李铖
李永康
JIANG Haijiao;LI Cheng;LI Yongkang(College of Mechanical and Vehicle Engineering,Taiyuan University of Technology,Taiyuan 030024,China;Post-doctoral Research Center,Taiyuan Economic,Technical Development Zone,Taiyuan 030032,China)
出处
《太原理工大学学报》
CAS
北大核心
2023年第3期570-576,共7页
Journal of Taiyuan University of Technology
基金
国家自然科学基金资助项目(51905370)
中国博士后科学基金资助项目(2020M680914)。
关键词
平面流铸
冷却辊
热变形
神经网络预测
恒间距控制
planar flow casting
cooling roller
thermal deformation
neural network prediction
constant gap control
