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International Journal on Interactive Design and Manufacturing (IJIDeM)
Erschienen in: International Journal on Interactive Design and Manufacturing (IJIDeM) 2/2023

15.07.2022 | Original Paper

Correlation analysis between looper system and strip width narrowing in hot strip rolling

verfasst von: Feng-wei Jing, Zhao-yu Chen, Jie Li, Qiang Guo

Erschienen in: International Journal on Interactive Design and Manufacturing (IJIDeM) | Ausgabe 2/2023

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Abstract

During the hot rolling industrial production process, strip width narrowing is the main quality problem of product. In this paper, it is obtained through experimental analysis that incoming material temperature, roll eccentricity and the control accuracy of looper equipment are the three main factors leading to strip width narrowing in the hot rolling finishing area. In order to deal with the multi-source data, a fault diagnosis model based on Long Short-Term Memory (LSTM) model and data fusion is proposed, and the diagnosis results are verified. The proposed model is a single LSTM model structure using data fusion twice. Compared with multiple LSTM integrated model structure using data fusion once and multiple Convolutional Neural Networks (CNN) + LSTM integrated model structure that does not use data fusion technology, the model proposed in this paper makes a great improvement in the accuracy of fault detection. This model can be applied to hot strip finishing rolling to determine the causes of width narrowing.
Vorheriger Artikel Revisiting requirement engineering for intelligent manufacturing
Nächster Artikel Experimental and predictive modelling in dry micro-drilling of titanium alloy using Ti–Al–N coated carbide tools

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Literatur
1.
Huang, Z.D.: Cause analysis and control measures for rolling narrow in 1500 mm hot strip rolling line. Steel Roll. 35(01), 79–81+94 (2018)
2.
He, F., Xiao, H.F.: Cause analysis of head width narrow of hot rolled strip based on feature selection of information entropy. Chin. J. Eng. 37(S1), 45–50 (2015)
3.
Li, S.J., Ren, L.: Control function optimization of pinch roll of hot rolling coiler. Hebei Metall. 4, 5 (2020)
4.
Liu, D., Hu, Y., Zhang, B.Z., Chen, K., Wu, W.J.: Cause analysis of head width changing to narrow of strip during rolling on 1780 mm hot strip finishing mill and its improvement. Metall. Ind. Autom. 36(01), 62–66 (2012)
5.
Yang, P., Chen, Z.J.: Improvement of looper control system for hot rolled strip head narrowing. China Metall. 28(12), 36–40+44 (2018)
6.
Zhang, W.B.: Research and optimization of relations between loops and narrowing down of strips’ head in HSM. Metall. Ind. Autom. 40(06), 66–69 (2016)
7.
Chen, Z.R., Wang, Z.: Analysis of the Width defects in FM area and the corresponding improvement solutions for 1580 mm hot continuous rolling mill. In: Proceedings of China Iron & Steel Annual Meeting, Shanghai, pp. 271–275 (2015)
8.
Guo, Y.B.: Research and application of redundant tension detection system for looper tension control of hot rolling mill. Autom. Instrum. 230(12), 133–135+138 (2018)
9.
Zhao, D.Q., Zong, S.Y., Zhang, F.: High-frequency oscillating detection for hydraulic loop and suppression method. Mach. Tool Hydraul. 43(15), 94–97 (2015)
10.
Chen, G.F.: Analysis and improvement for water beam mark during heavy plate rolling. In: Proceedings of China Iron & Steel Annual Meeting, Beijing, pp. 43–50 (2019)
11.
Li, F.J., Liu, H.F., Zhang, X.: On-line identification of roll eccentricity based on PSO-RBF neural network. J. Iron Steel Res. 29(11), 906–911 (2017)
12.
Qin, H.B., Li, J.H., Dong, Y.X., Wang, J.J.: Analysis and improvement of narrowing of hot rolled strip in short process. Steel Roll. 38(04), 94–98 (2021)
13.
Li, D., Wu, Y.H., Shi, H.T., Liu, J.C., Tan, S.B.: Study on roll eccentricity compensation method based on swarm intelligence identification. Control Eng. China 27(08), 9 (2020)
14.
Niu, M.K., Yang, W.D., Zhang, L.J.: Dynamic rolling eccentricity measurement method of roller frame. Mach. Tool Hydraul. 39(04), 108–110 (2011)
15.
Ding, X.L., Wu, B.H., Long, J.J., Xing, Z.P., Huang, Z.J.: The application of case-based reasoning in fault diagnosis of looper’s hydraulic system in large scale steel rolling line. Mach. Tool Hydraul. 5, 247–249 (2003)
16.
Sun, Y.K.: Model and Control of Hot Strip Rolling. Metallurgical Industry Press, Beijing (2002)
17.
Zhu, Y.B., Zhang, F., Zhang, Y.J., Wang, Z.Q.: Particle swarm optimized neural network for strip crown prediction model research. Metall. Ind. Autom. 43(02), 11–15+28 (2019)
18.
Cai, Z.X.: Application of artificial intelligence in metallurgy automation. J Metall. Ind. Autom. 39(1), 1–8 (2015)MathSciNet
19.
Wei, L.X., Wei, X.Y., Sun, H., Wang, H.: Prediction of aluminum hot rolling force based on deep network. Chin. J. Nonferr. Metals 28(10), 2070–2076 (2018)
20.
Peng, L.G., Wang, D.G., Li, J., Xing, J.F., Gong, D.Y.: Data-driven adaptive setting algorithm for coiling temperature model parameter. Chin. J. Eng. 42(06), 778–786 (2020)
21.
Khaleghi, B., Khamis, A., Karray, F.O., Razavi, S.N.: Multisensor data fusion: a review of the state-of-the-art. Inf. Fus. 14(1), 28–44 (2013)CrossRef
22.
Zhang, H.K., Chen, N.S., Dai, Z.X., Fan, G.Y.: A multi-level data fusion localization algorithm for SLAM. Robot 43(06), 641–652 (2021)
23.
Qin, S.T., Huang, C., Tian, J.Y., Yang, Y., Wei, H.: Research and application of multi-source big data fusion in power grid. Chin. J. Electron Dev. 44(02), 480–485 (2021)
24.
Ren, J.T., Tang, B.P., Yong, B., Deng, L., Zhang, K.: Wind turbine gearbox fault warning based on depth variational autoencoders network fusion SCADA data. Acta Energ. Solar. Sin. 42(04), 403–408 (2021)
25.
Federico, C.: A review of data fusion techniques. Sci. World J. 2, 1–19 (2013)
26.
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRef
27.
Liu, Y., Gao, Z.Y., Zhou, X.M., Zhang, Q.D.: Industrial data-driven intelligent forecast for chatter of cold rolling of thin strip with LSTM recurrent neural network. J. Mech. Eng. 56(11), 121–131 (2020)CrossRef
28.
Zhang, P., Yang, T., Liu, Y.N., Fan, Z.Y., Duan, Z.B.: Feature extraction and prediction of QAR data based on CNN-LSTM. Appl. Res. Comput. 36(10), 2958–2961 (2019)
29.
Lu, J.X., Zhang, Q.P., Yang, H.Z., Tu, M.F., Lu, J.J., Peng, H.: Short-term load forecasting method based on CNN-LSTM hybrid neural network model. Autom. Electr. Power Syst. 43(08), 131–137 (2019)
30.
Jia, C.Y., Kang, K.X., Gao, W., Yang, D., Chen, L.J., Ai, C.: Fault prediction of electro-hydraulic servo valve based on CNN+LSTM neural network. Chin. Hydraul. Pneumat. 12, 173–181 (2020)
Metadaten
Titel
Correlation analysis between looper system and strip width narrowing in hot strip rolling
verfasst von
Feng-wei Jing
Zhao-yu Chen
Jie Li
Qiang Guo
Publikationsdatum
15.07.2022
Verlag
Springer Paris
Erschienen in
International Journal on Interactive Design and Manufacturing (IJIDeM) / Ausgabe 2/2023
Print ISSN: 1955-2513
Elektronische ISSN: 1955-2505
DOI
https://doi.org/10.1007/s12008-022-00940-y

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