Temperature Control Optimization in a Steel‐Making Continuous Casting Process Using a Multimodal Deep Learning Approach

[1]  John Norrish,et al.  Artificial neural networks for modelling the mechanical properties of steels in various applications , 2005 .

[2]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[3]  Jin Cheng,et al.  Application of the response surface methods to solve inverse reliability problems with implicit response functions , 2009 .

[4]  Michael R. Harwell,et al.  Experimental Design and Data Analysis in Computer Simulation Studies in the Behavioral Sciences , 2017 .

[5]  Jong-Yeon Hwang,et al.  In-depth study of mold heat transfer for the high speed continuous casting process , 2016, Metals and Materials International.

[6]  Bayu Adhi Tama,et al.  Tree-based classifier ensembles for early detection method of diabetes: an exploratory study , 2019, Artificial Intelligence Review.

[7]  F. R. Camisani-Calzolari,et al.  Secondary Cooling Zone Control in Continuous Casting , 1999 .

[8]  Seungchul Lee,et al.  A Neural Network Model for Material Degradation Detection and Diagnosis Using Microscopic Images , 2019, IEEE Access.

[9]  Bertrand Iooss,et al.  Numerical studies of space-filling designs: optimization of Latin Hypercube Samples and subprojection properties , 2013, J. Simulation.

[10]  Man Yeong Ha,et al.  Numerical analysis of secondary cooling and bulging in the continuous casting of slabs , 2001 .

[11]  Peter Hodgson,et al.  Extrapolative prediction of the hot strength of austenitic steels with a combined constitutive and ANN model , 2000 .

[12]  Nirupam Chakraborti,et al.  A study of the continuous casting mold using a pareto-converging genetic algorithm , 2001 .

[13]  J. K. Brimacombe,et al.  Crack formation in the continuous casting of steel , 1977 .

[14]  Y. Meng,et al.  Heat-transfer and solidification model of continuous slab casting: CON1D , 2003 .

[15]  Yu Yao,et al.  Detection of a casting defect tracked by deep convolution neural network , 2018, The International Journal of Advanced Manufacturing Technology.

[16]  Ke Xu,et al.  Surface Detection of Continuous Casting Slabs Based on Curvelet Transform and Kernel Locality Preserving Projections , 2013 .

[17]  Wang Xudong,et al.  Development of prediction method for abnormalities in slab continuous casting using artificial neural network models , 2006 .

[18]  D. Mills Jamie.  Using Computer Simulation Methods to Teach Statistics: A Review of the Literature , 2002 .

[19]  Quan-Ke Pan,et al.  An effective co-evolutionary artificial bee colony algorithm for steelmaking-continuous casting scheduling , 2016, Eur. J. Oper. Res..

[20]  H. K. D. H. Bhadeshia,et al.  The yield and ultimate tensile strength of steel welds , 1997 .

[21]  Zheng O'Neill,et al.  A methodology for meta-model based optimization in building energy models , 2012 .

[22]  J. A. Spim,et al.  A solidification heat transfer model and a neural network based algorithm applied to the continuous casting of steel billets and blooms , 2005 .

[23]  P. Saravanakumar,et al.  Prediction of Mechanical Properties of Low Carbon Steel in Hot Rolling Process Using Artificial Neural Network Model , 2012 .

[24]  Lorenz T. Biegler,et al.  Finite difference heat-transfer modeling for continuous casting , 1990 .

[25]  Bouhouche Salah,et al.  Adaptive support vector machine-based surface quality evaluation and temperature monitoring. Application to billet continuous casting process , 2013 .