1 National Engineering Research Center for Advanced Rolling and Intelligent Manufacturing, University of Science and Technology Beijing, Beijing 100083, China; 2 China Academy of Machinery Science and Technology, Beijing 100044, China
MDA-JITL model for on-line mechanical property prediction
1 National Engineering Research Center for Advanced Rolling and Intelligent Manufacturing, University of Science and Technology Beijing, Beijing 100083, China; 2 China Academy of Machinery Science and Technology, Beijing 100044, China
摘要 Mechanical performance prediction is the key to the transformation and upgrading of steel enterprises to intelligent manufacturing. Due to time-varying manufacturing data, the traditional prediction model of mechanical properties of hotrolled strip may cause performance degradation or even failure in its use. An MDA-JITL model was thus proposed to handle the modeling problem of complex time-varying data. Relevant parameters were first chosen and normalized. Then, a distance measurement method combining the importance of data attributes and time characteristics was designed to select the most suitable samples for on-line local modeling. After that, using the chosen dataset, a linear local model was created to predict target sample. Finally, an uncertainty evaluation method was designed to evaluate the uncertainty of prediction results. Furthermore, the appropriate dataset partition and off-line simulation experiment scheme were created based on the peculiarities of hot-rolling production. The suggested model performs much better than the classic global model when applied to actual production data from a steel plant. The stability of its prediction accuracy is demonstrated in a simulation prediction for up to five months. Moreover, there is a high link between the uncertainty evaluation metrics and the prediction error of the model, reducing the field sampling rate by 30% in industrial applications in the latest year.
Abstract：Mechanical performance prediction is the key to the transformation and upgrading of steel enterprises to intelligent manufacturing. Due to time-varying manufacturing data, the traditional prediction model of mechanical properties of hotrolled strip may cause performance degradation or even failure in its use. An MDA-JITL model was thus proposed to handle the modeling problem of complex time-varying data. Relevant parameters were first chosen and normalized. Then, a distance measurement method combining the importance of data attributes and time characteristics was designed to select the most suitable samples for on-line local modeling. After that, using the chosen dataset, a linear local model was created to predict target sample. Finally, an uncertainty evaluation method was designed to evaluate the uncertainty of prediction results. Furthermore, the appropriate dataset partition and off-line simulation experiment scheme were created based on the peculiarities of hot-rolling production. The suggested model performs much better than the classic global model when applied to actual production data from a steel plant. The stability of its prediction accuracy is demonstrated in a simulation prediction for up to five months. Moreover, there is a high link between the uncertainty evaluation metrics and the prediction error of the model, reducing the field sampling rate by 30% in industrial applications in the latest year.
Fei-fei Li,An-rui He,Yong Song, et al. MDA-JITL model for on-line mechanical property prediction[J]. Journal of Iron and Steel Research International, 2023, 30(03): 504-515.
2023, Vol. 30 
