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Prediction of hot-rolled strip crown based on Boruta and extremely randomized trees algorithms

Journal of Iron and Steel Research International 2023年第5期30卷 1022-1031页

作者： Li Wang Song-lin He Zhi-ting Zhao Xian-du Zhang School of Mechanical and Power Engineering Shenyang University of Chemical TechnologyShenyang 110142LiaoningChina

The quality of hot-rolled steel strip is directly affected by the strip *** machine learning models have shown limitations in accurately predicting the strip crown,particularly when dealing with imbalanced *** limitation results in poor production quality and efficiency,leading to increased production ***,a novel strip crown prediction model that uses the Boruta and extremely randomized trees(Boruta-ERT)algorithms to address this issue was *** improve the accuracy of our model,we utilized the synthetic minority over-sampling technique to balance the imbalance data *** Boruta-ERT prediction model was then used to select features and predict the strip *** the 2160 mm hot rolling production lines of a steel plant serving as the research object,the experimental results showed that 97.01% of prediction data have an absolute error of less than 8 *** level of accuracy met the control requirements for strip crown and demonstrated significant benefits for the improvement in production quality of steel strip.

关键词： hot-rolled strip Data improvement strip crown Feature selection Boruta algorithm Extremely randomized trees algorithm

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Deep learning for predictive mechanical properties of hot-rolled strip in complex manufacturing systems

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International Journal of Minerals,Metallurgy and Materials 2023年第6期30卷 1093-1103页

作者： Feifei Li Anrui He Yong Song Zheng Wang Xiaoqing Xu Shiwei Zhang Yi Qiang Chao Liu National Engineering Research Center for Advanced Rolling and Intelligent Manufacturing University of Science and Technology BeijingBeijing 100083China China Academy of Machinery Science and Technology Beijing 100044China

Higher requirements for the accuracy of relevant models are put throughout the transformation and upgrade of the iron and steel sector to intelligent *** has been difficult to meet the needs of the field with the usual prediction model of mechanical properties of hotrolled *** data and difficult parameter adjustment limit deep learning models based on multi-layer networks in practical applications;besides,the limited discrete process parameters used make it impossible to effectively depict the actual strip processing *** order to solve these problems,this research proposed a new sampling approach for mechanical characteristics input data of hot-rolled strip based on the multi-grained cascade forest(gcForest)*** to the characteristics of complex process flow and abnormal sensitivity of process path and parameters to product quality in the hot-rolled strip production,a three-dimensional continuous time series process data sampling method based on time-temperature-deformation was *** basic information of strip steel(chemical composition and typical process parameters)is fused with the local process information collected by multi-grained scanning,so that the next link’s input has both local and global ***,in the multi-grained scanning structure,a sub sampling scheme with a variable window was designed,so that input data with different dimensions can get output characteristics of the same dimension after passing through the multi-grained scanning structure,allowing the cascade forest structure to be trained ***,actual production data of three steel grades was used to conduct the experimental *** results revealed that the gcForest-based mechanical property prediction model outperforms the competition in terms of comprehensive performance,ease of parameter adjustment,and ability to sustain high prediction accuracy with fewer samples.

关键词： hot-rolled strip prediction of mechanical properties deep learning multi-grained cascade forest time series feature extraction variable window subsampling

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Prediction model for mechanical properties of hot-rolled strips by deep learning

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Journal of Iron and Steel Research International 2020年第9期27卷 1045-1053页

作者： Wei-gang Li Lu Xie Yun-tao Zhao Zi-xiang Li Wen-bo Wang Engineering Research Center of Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and TechnologyWuhan 430081HubeiChina Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and TechnologyWuhan 430081HubeiChina Hubei Province Key Laboratory of Systems Science in Metallurgical Process Wuhan University of Science and TechnologyWuhan 430081HubeiChina

The prediction of the mechanical properties of hot-rolled strips is a very complex,highly dimensional and nonlinear problem,and the published models might lack reliability,practicability and ***,a new model was proposed for predicting the mechanical properties of hot-rolled strips by deep ***,the one-dimensional numerical data were transformed into two-dimensional data for expressing the complex interaction between the influencing ***,a new convolutional network was proposed to establish the prediction model of tensile strength of hot-rolled strips,and an improved inception module was introduced into this network to abstract features from different *** comparative experiments were carried out to find the optimal network structure and its ***,the prediction experiments were carried out on different models to evaluate the performance of the new convolutional network,which includes the stepwise regression,ridge regression,support vector machine,random forest,shallow neural network,Bayesian neural network,deep feed-forward network and improved LeNet-5 convolutional neural *** results show that the proposed convolutional network has better prediction accuracy of the mechanical properties of hot-rolled strips compared with other models.

关键词： Deep learning Convolutional neural network Mechanical property hot-rolled strip

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Application of Fractal Dimension Feature to Recognition of Surface Defects on hot-rolled strips

Application of Fractal Dimension Feature to Recognition of S...

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2012 International Conference on Mechanical Engineering and Materials(ICMEM 2012)

作者： Ke Xu Peng Zhou Chaolin YANG National Engineering Research Centre for Advanced Rolling Technology University of Science and Technology Beijing

Because steel strips are covered with scales and water during hot-rolling,it is difficult to recognize the defects from images of hot-rolled strips through conventional *** and characteristics of fractal dimensions were studied,and computation of the fractal dimensions of the defect images with Peleg Covered Carpet is *** dimensions of piecewise linearly transformed and smoothed images were used as features for classification of *** features were inputted to train the Ada Boost *** with samples of pimples,shells and scales from a real surface inspection system of hot-rolled strips showed that it is effective to recognized scales from other defects,and the total classification rate of this method is higher than 90%.

关键词： Fractal dimension hot-rolled strip Surface inspection

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hot-rolled strips of up to 19 mm in thickness and their processing to helically welded large diameter pipes of grade X80

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Baosteel Technical Research 2010年第S1期4卷 56-页

作者： Volker FLAXA Franz Martin KNOOP Salzgitter Mannesmann Forschung GmbH Germany Salzgitter Mannesmann Groβrohr GmbH Germany

hot-rolled wide strip for production of large diameter,heavy gauged(up to 19 mm) helical line pipe grade X80 was a priority development over the last three ***,texture and mechanical properties of strips have been *** the welding conditions have been *** favourable microstructure is achieved by the proper selection of an appropriate chemical composition of low carbon content and increased niobium micro alloying in combination with suitable strictly controlled hot-rolling *** addition of niobium in combination with the adjustment of other alloying elements increases the recrystallisation stop temperature and thus makes it possible to apply a high temperature processing(HTP) *** homogeneous bainitic microstructure across the strip gauge is then formed during accelerated cooling on the run-out table of the hot-rolling *** results indicated excellent properties of these hot strips which make it suitable for spiral pipes of grade X80 for example 18.9mm×Φ1 220 mm at dimension.

关键词： hot-rolled strip helically welded pipe X80 up to 19 mm wall thickness salzgitter

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Steckel mill as a green steel strip and plate production technology

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Baosteel Technical Research 2010年第S1期4卷 106-页

作者： M ROTTI P HUGHES-NARBOROUGH Siemens VAI Metals Technologies Sheffield S9 1XU UK

The Steckel mill,a long established solution for the economical production of relatively small volumes of hot rolled strip,has been rejuvenated in recent years by a range of new applications,boosted by the need of improving the energetic efficiency of the rolling *** traditional advantages of the Steckel mill in terms of flexibility and reduced capital and operational costs are now enhanced by technological developments that have significantly expanded its application range into the combined production of strip and plate and improved the product *** increased awareness of the necessity of a sustainable growth in the steel industry has stimulated the development of process solutions with an improved efficiency in the use of natural resources,lower carbon emissions and increased *** Steckel mills are an adequate response to the trend towards low energy strip and plate production,in particular in their plate-Steckel mill *** VAI have played a key role in the innovation and transformation of the Steckel mill concept,with a number of recent installations,presented in this paper from the point of view of their contribution to the development of greener steel rolling technologies.

关键词： Steckel mill hot-rolled strip efficiency increased yield plate-steckel mill,sustainable

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