Abstract：The traditional testing method of mechanical properties of steel products in a hot strip mill is an experimental method based on the statistical random sampling theory. The test samples are cut from the tail end of a hot coil and then tested in a laboratory. Test results represent the performance of the whole batch of steel products. Due to the long steel production process and certain fluctuations in the control parameters of the production process, the traditional mechanical property testing method cannot reflect the mechanical properties of each coil of strip steel, and the representativeness of the tested samples is not sufficient. With the rapid development of industrial Internet, big data and artificial intelligence technologies, especially the development and application of industrial big data-related technologies, new approaches are provided for solving this problem. Taking the realization of on-line steel mechanic properties prediction (On-line MPP)of hot tandem rolling products of Shandong Iron and Steel Group Rizhao Co., Ltd. as the test object, and based on the key control process parameter data of the whole process of the hot rolling products, the neural network, random forest and other algorithms are used to establish carbon structural and low-alloy high-strength construction steel mechanical properties prediction model, built a mechanical properties prediction system for hot-rolled products based on industrial big data, including data acquisition, data cleaning, model training, result analysis and online application. The on-line MPP has put into application for over two years and it has demonstrated well precision,high stability and reliability. The prediction accuracy of the model is within ±6%, the sample volume reaches more than 90%, and the MAPE (average absolute percentage error) ≤4%, which is lower than the reproducibility detection level., it can replace sampling inspection. As a result, shorten product inspection cycle, the mechanical properties of the product can be grasped after rolling. The system has become an important part in the production and operation process.
孙卫华, 焦吉成, 李率民, 崔健, 曹金生, 王猛. 热连轧钢材力学性能在线预报的研究与应用[J]. 钢铁, 2022, 57(8): 168-176.
SUN Wei-hua, JIAO Ji-cheng, LI Shuai-min, CUI Jian, CAO Jin-sheng, WANG Meng. Research and application of on-line steel mechanical property prediction in a hot strip mill[J]. Iron and Steel, 2022, 57(8): 168-176.
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2022, Vol. 57 
