Deep learning-based prediction framework of temperature control time for wide-thick slab hot rolling production

Al-Afandy, 2019, Artificial neural networks optimization and convolution neural networks to classifying images in remote sensing: A Review, ACM, 1 Calvo-Bascones, 2023, Advanced Prognosis methodology based on behavioral indicators and Chained Sequential Memory Neural Networks with a diesel engine application, Computers in Industry, 144, 10.1016/j.compind.2022.103771 Carretero Olalla, 2012, Influence of rolling temperature and cooling rate on microstructure and properties of pipeline steel grades, Materials Science Forum, 706–709, 2710, 10.4028/www.scientific.net/MSF.706-709.2710 Choi, 2022, Prediction of molten steel flow in a tundish with water model data using a generative neural network with different clip sizes, Journal of Mechanical Science and Technology, 36, 749, 10.1007/s12206-022-0123-9 Chen, 2021, Optimization of dephosphorization parameter in consteel electric arc furnace using rule set model, Steel Research International, 92, 2000719, 10.1002/srin.202000719 Chen, 2022, Temperature distribution prediction in control cooling process with recurrent neural network for variable-velocity hot rolling strips, The International Journal of Advanced Manufacturing Technology, 120, 7533, 10.1007/s00170-022-09065-8 Chen, 2021, Application of data-driven models to predictive maintenance: Bearing wear prediction at TATA steel, Expert Systems with Applications, 186, 10.1016/j.eswa.2021.115699 Chen, 2020, Strain-induced precipitation in Ti microalloyed steel by two-stage controlled rolling process, Journal of Materials Research and Technology, 9, 15759, 10.1016/j.jmrt.2020.11.040 Koller, D., Sahami, M., Toward optimal feature selection Proceedings of the 13th International Conference on Machine Learning. Bari, Italy: Morgan Kaufmann; 1996. p. 284-92. Dang, 2021, Predicting tensile-shear strength of nugget using M5P model tree and random forest: An analysis, Computers in Industry, 124, 10.1016/j.compind.2020.103345 Dias, 2021, A cloud-based condition monitoring system for fault detection in rotating machines using PROFINET process data, Computers in Industry, 126, 10.1016/j.compind.2021.103394 Dong, 2022, Prediction and analysis of key parameters of head deformation of hot-rolled plates based on artificial neural networks, Journal of Manufacturing Processes, 77, 282, 10.1016/j.jmapro.2022.03.022 García-Torres, 2016, High-dimensional feature selection via feature grouping: A Variable Neighborhood Search approach, Inform Sciences, 326, 102, 10.1016/j.ins.2015.07.041 Hussain, 2020, Trade-off between exploration and exploitation with genetic algorithm using a novel selection operator, Complex & Intelligent Systems, 6, 1, 10.1007/s40747-019-0102-7 Hwang, 2020, Hybrid model of mathematical and neural network formulations for rolling force and temperature prediction in hot rolling processes, IEEE Access, 8, 153123, 10.1109/ACCESS.2020.3016725 Ji, 2022, A machine learning and genetic algorithm-based method for predicting width deviation of hot-rolled strip in steel production systems, Inform Sciences, 589, 360, 10.1016/j.ins.2021.12.063 Laukhin, 2019, The elaboration of modernized technology of controlled rolling directed at the formation of high strengthening and viscous qualities in HSLA steel, Solid State Phenomena, 291, 13, 10.4028/www.scientific.net/SSP.291.13 Lei, 2019, Research and application of a rolling gap prediction model in continuous casting, Metals-Basel, 9, 380, 10.3390/met9030380 Lee, 2022, Surface roughness prediction and optimization in the REMF process using an integrated DBN-GA approach, International Journal of Advanced Manufacturing Technology, 121, 5931, 10.1007/s00170-022-09652-9 Li, 2022, Least squares twin support vector machines to classify end-point phosphorus content in BOF steelmaking, Metals-Basel, 12, 268, 10.3390/met12020268 Li, 2019, A novel deep stacking least squares support vector machine for rolling bearing fault diagnosis, Computers in Industry, 110, 36, 10.1016/j.compind.2019.05.005 Li, 2019, A deep learning driven method for fault classification and degradation assessment in mechanical equipment, Computers in Industry, 104, 1, 10.1016/j.compind.2018.07.002 Ling, 2022, Research on remaining service life prediction of platform screen doors system based on genetic algorithm to optimise BP neural network, Enterprise Information Systems-Uk, 16, 1 Liu, 2022, Flatness prediction of cold rolled strip based on deep neural network with improved activation function, Sensors-Basel, 22, 656, 10.3390/s22020656 Malley, 2022, Predictability of mechanical behavior of additively manufactured particulate composites using machine learning and data-driven approaches, Computers in Industry, 142, 10.1016/j.compind.2022.103739 Micci-Barreca, 2001, A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems, ACM SIGKDD Explorations Newsletter, 1, 27, 10.1145/507533.507538 Morariu, 2020, Machine learning for predictive scheduling and resource allocation in large scale manufacturing systems, Computers in Industry, 120, 103244, 10.1016/j.compind.2020.103244 Peng, 2022, Industrial big data-driven mechanical performance prediction for hot-rolling steel using lower upper bound estimation method, Journal of Manufacturing Systems, 65, 104, 10.1016/j.jmsy.2022.08.014 Phull, 2020, An application of decision tree-based twin support vector machines to classify dephosphorization in BOF steelmaking, Metals-Basel, 10, 25, 10.3390/met10010025 Reshef, 2011, Detecting novel associations in large data sets, Science (American Association for the Advancement of Science), 334, 1518, 10.1126/science.1205438 Son, 2022, Integrated framework for estimating remaining useful lifetime through a deep neural network, Applied Soft Computing, 122, 10.1016/j.asoc.2022.108879 Tian, 2022, Performance prediction of a cryogenic organic Rankine cycle based on back propagation neural network optimized by genetic algorithm, Energy, 254, 10.1016/j.energy.2022.124027 Wang, 2021, Prediction of blast furnace hot metal temperature based on support vector regression and extreme learning machine, Chinese Journal of Engineering, 43, 569 Xin, 2022, A hybrid modeling method based on expert control and deep neural network for temperature prediction of molten steel in LF, ISIJ International, 62, 532, 10.2355/isijinternational.ISIJINT-2021-251 Xu, 2021, Prediction of springback in local bending of hull plates using an optimized backpropagation neural network, Mechanical Sciences, 12, 777, 10.5194/ms-12-777-2021 Xu, 2019, Control technology of end-point carbon in converter steelmaking based on functional digital twin model, Chinese Journal of Engineering, 41, 521 Yang, 2022, Forecasting of iron ore sintering quality index: A latent variable method with deep inner structure, Computers in Industry, 141, 10.1016/j.compind.2022.103713 Yuan, 2023, An improved estimation of distributed algorithm for a single machine scheduling problem with re-entrant and group features, Operations Research and Management Science, 1 Zhang, 2022, Industrial cyber-physical system driven intelligent prediction model for converter end carbon content in steelmaking plants, Journal of Industrial Information Integration, 28, 10.1016/j.jii.2022.100356 Zhang, 2021, Optimization of backpropagation neural network under the adaptive genetic algorithm, Complexity, 2021, 1 Zhang, 2020, Application of intelligent manufacturing technology in hot rolling production Lin——Baosteel 1580 hot rolling demonstration production line, China Mechanical Engineering, 31, 246 Zhang, 2022, An integrated model of rolling force for extra-thick plate by combining theoretical model and neural network model, Journal of Manufacturing Processes, 75, 100, 10.1016/j.jmapro.2021.12.063 Zhang, 2020, Erosion model of hearth based on big data technology, Iron & Steel, 55, 160 Zhou, 2022, Controlled rolling of X80 pipeline steel in the austenite recrystallization temperature region and its effect on the microstructure and mechanical properties, Steel Research International, 93, 2100331, 10.1002/srin.202100331