Skip to main content
Top
Published in: Metallurgist 9-10/2024

13-03-2024

Artificial Intelligence and Machine Learning In Metallurgy. Part 2. Application Examples

Authors: P. Yu. Zhikharev, A. V. Muntin, D. A. Brayko, M. O. Kryuchkova

Published in: Metallurgist | Issue 9-10/2024

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The paper offers a detailed description of the application and significance of machine learning methods during various processing stages of modern metallurgy. The relevance of this topic is based on the significant positive technical and economic effects from the use of machine learning noted by both Russian and world-leading manufacturers in the field of metallurgy.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference A. V. Muntin, P. Yu. Zhikharev, A. G. Ziniagin, and D. A. Brayko, “Artificial intelligence and machine learning in metallurgy. Part 1. Methods and algorithms,” Metallurgist, 6, 124–130 (2023).CrossRef A. V. Muntin, P. Yu. Zhikharev, A. G. Ziniagin, and D. A. Brayko, “Artificial intelligence and machine learning in metallurgy. Part 1. Methods and algorithms,” Metallurgist, 6, 124–130 (2023).CrossRef
2.
go back to reference F. Yu, Y. Suo, X. Zang, et al., “Data mining in blast furnace smelting parameter,” Applied Mechanics and Materials, 303–306, 1093–1096 (2013). F. Yu, Y. Suo, X. Zang, et al., “Data mining in blast furnace smelting parameter,” Applied Mechanics and Materials, 303–306, 1093–1096 (2013).
3.
go back to reference Bjork, Holopainen, Wikstron, et al., “Analysis of blast furnace time series data with ANFIS,” Turku Center for Computer Science [TUCS], 10 (2013). Bjork, Holopainen, Wikstron, et al., “Analysis of blast furnace time series data with ANFIS,” Turku Center for Computer Science [TUCS], 10 (2013).
4.
go back to reference J. Jong-Hag, “Data mining application of six-sigma project,” in: POSCO, Pohang South Korea, SUGI 29 Solutions, pp. 186–29. J. Jong-Hag, “Data mining application of six-sigma project,” in: POSCO, Pohang South Korea, SUGI 29 Solutions, pp. 186–29.
6.
go back to reference M. Wauters, V. Tusset, J. Knevels, et al., “New sampling and analysis method for dynamic end-point control at BOF process,” Metallurgical Analysis, 26, 8–15 (2006). M. Wauters, V. Tusset, J. Knevels, et al., “New sampling and analysis method for dynamic end-point control at BOF process,” Metallurgical Analysis, 26, 8–15 (2006).
7.
go back to reference R. Meilland, M. Wauters, J. Knevels, et al., “Dynamic end-point control in BOF through a fast and simultaneous determination of the steel/slag composition,” Revue De Métallurgie, 103, No. 9, 374–380 (2006).CrossRef R. Meilland, M. Wauters, J. Knevels, et al., “Dynamic end-point control in BOF through a fast and simultaneous determination of the steel/slag composition,” Revue De Métallurgie, 103, No. 9, 374–380 (2006).CrossRef
8.
go back to reference J. Maiolo and D. Zuliani, “BOF end-point prediction,” Metal Producing and Processing, 46, No. 6, 15–18 (2008). J. Maiolo and D. Zuliani, “BOF end-point prediction,” Metal Producing and Processing, 46, No. 6, 15–18 (2008).
9.
go back to reference S. Aki, R. Jari, L. Jarmo, et al., “Data-driven multivariate analysis of basic oxygen furnace used in steel industry,” IFACPapersOnLine, 48–17, 177–182 (2015). S. Aki, R. Jari, L. Jarmo, et al., “Data-driven multivariate analysis of basic oxygen furnace used in steel industry,” IFACPapersOnLine, 48–17, 177–182 (2015).
10.
go back to reference M. Kanemot, H. Yamane, T. Yoshida, et al., “An application of expert system to LD converter process,” ISIJ Int., 30, No. 2, 128–135 (1990).CrossRef M. Kanemot, H. Yamane, T. Yoshida, et al., “An application of expert system to LD converter process,” ISIJ Int., 30, No. 2, 128–135 (1990).CrossRef
11.
go back to reference G. Carayannis, “Artificial intelligence and expert systems in the steel industry,” JOM, 45, No. 10, 43–51 (1993).CrossRef G. Carayannis, “Artificial intelligence and expert systems in the steel industry,” JOM, 45, No. 10, 43–51 (1993).CrossRef
12.
go back to reference T. Takawa, K. Katayama, and M. Hoteiya, “Development of a mathematical model of end point control system for top and bottom blowing process in BOF,” Tetsu-to-Hagane, 73, No. 7, 836–843 (2009).CrossRef T. Takawa, K. Katayama, and M. Hoteiya, “Development of a mathematical model of end point control system for top and bottom blowing process in BOF,” Tetsu-to-Hagane, 73, No. 7, 836–843 (2009).CrossRef
13.
go back to reference H. Fei, X. Chai, and Z. Zhu, “Prediction of oxygen-blowing volume in BOF steelmaking process based on BP neural network and incremental learning,” High Temperature Materials and Processes, 41, 403–416 (2022).CrossRef H. Fei, X. Chai, and Z. Zhu, “Prediction of oxygen-blowing volume in BOF steelmaking process based on BP neural network and incremental learning,” High Temperature Materials and Processes, 41, 403–416 (2022).CrossRef
14.
go back to reference X. Z. Wang and M. Han, “Causality-based CBR model for static control of converter steelmaking,” Journal Iron and Steel Research, 51, No. 4, 593–598 (2011). X. Z. Wang and M. Han, “Causality-based CBR model for static control of converter steelmaking,” Journal Iron and Steel Research, 51, No. 4, 593–598 (2011).
15.
go back to reference H. Zhao, X. M. Yi, H. J. Wang, et al., “Prediction model research of oxygen consumption in BOF,” Computer Simulation, 34, No. 1, 380–383 (2017). H. Zhao, X. M. Yi, H. J. Wang, et al., “Prediction model research of oxygen consumption in BOF,” Computer Simulation, 34, No. 1, 380–383 (2017).
16.
go back to reference C. Gao, M. G. Shen, X. P. Liu, et al., “End-point static control of basic oxygen furnace (BOF) steelmaking based on wavelet transform weighted twin support vector regression,” Complexity, 2019, No. 6, Article ID 7408725 (2019). C. Gao, M. G. Shen, X. P. Liu, et al., “End-point static control of basic oxygen furnace (BOF) steelmaking based on wavelet transform weighted twin support vector regression,” Complexity, 2019, No. 6, Article ID 7408725 (2019).
17.
go back to reference A. L. Li, D. Z. Zhao, Z. B. Guo, et al., “Prediction of converter oxygen consumption in improved deep belief network,” China Measurement & Test, 46, No. 6, 1–6 (2020). A. L. Li, D. Z. Zhao, Z. B. Guo, et al., “Prediction of converter oxygen consumption in improved deep belief network,” China Measurement & Test, 46, No. 6, 1–6 (2020).
19.
go back to reference I. J. Cox, R. W. Lewis, R. S. Ransing, et al., “Application of neural computing in basic oxygen steelmaking,” J. Materials Processing Technology, 120, No. 1, 310–315 (2002).CrossRef I. J. Cox, R. W. Lewis, R. S. Ransing, et al., “Application of neural computing in basic oxygen steelmaking,” J. Materials Processing Technology, 120, No. 1, 310–315 (2002).CrossRef
20.
go back to reference A. M. F. Fileti, T. A. Pacianotto, and A. P. Cunha, “Neural modeling helps the BOS process to achieve aimed end-point conditions in liquid steel,” Eng. App. Artificial Intelligence, 19, No. 1, 9–17 (2006).CrossRef A. M. F. Fileti, T. A. Pacianotto, and A. P. Cunha, “Neural modeling helps the BOS process to achieve aimed end-point conditions in liquid steel,” Eng. App. Artificial Intelligence, 19, No. 1, 9–17 (2006).CrossRef
21.
go back to reference N. Rajesh, M. R. Khare, and S. K. Pabi, “Feed forward neural network for prediction of end blow oxygen in LD converter steel making,” Materials Research, 13, No. 1, 15–19 (2010).CrossRef N. Rajesh, M. R. Khare, and S. K. Pabi, “Feed forward neural network for prediction of end blow oxygen in LD converter steel making,” Materials Research, 13, No. 1, 15–19 (2010).CrossRef
22.
go back to reference M. Han, Y. Li, and Z. J. Cao, “Hybrid intelligent control of BOF oxygen volume and coolant addition,” Neurocomputing, 123, 415–423 (2014).CrossRef M. Han, Y. Li, and Z. J. Cao, “Hybrid intelligent control of BOF oxygen volume and coolant addition,” Neurocomputing, 123, 415–423 (2014).CrossRef
24.
go back to reference I. Grešovnik, T. Kodelja, R. Vertnik, et al., “Application of artificial neural networks to improve steel production process,” in: Proc. of the IASTED Intern. Conf. Art. Intel. and Soft Comp. (ASC 2012), June 25–27, Napoli, Italy. I. Grešovnik, T. Kodelja, R. Vertnik, et al., “Application of artificial neural networks to improve steel production process,” in: Proc. of the IASTED Intern. Conf. Art. Intel. and Soft Comp. (ASC 2012), June 25–27, Napoli, Italy.
25.
go back to reference Z. Sterjovski, D. Nolan, K. R. Carpenter, et al., “Artificial neural networks for modelling the mechanical properties of steels in various applications,” J. Mater. Process. Technol., 170, 536–544 (2005).CrossRef Z. Sterjovski, D. Nolan, K. R. Carpenter, et al., “Artificial neural networks for modelling the mechanical properties of steels in various applications,” J. Mater. Process. Technol., 170, 536–544 (2005).CrossRef
26.
go back to reference M. Brezocnik and U. Župerl, “Optimization of the continuous casting process of hypoeutectoid steel grades using multiple linear regression and genetic programming – an industrial study,” Metals, 11, 972 (2021).CrossRef M. Brezocnik and U. Župerl, “Optimization of the continuous casting process of hypoeutectoid steel grades using multiple linear regression and genetic programming – an industrial study,” Metals, 11, 972 (2021).CrossRef
27.
go back to reference P. J. García, V. M. González, J. C. Álvarez, et al., “A new predictive model of centerline segregation in continuous cast steel slabs by using multivariate adaptive regression splines approach,” Materials, 8, 3562–3583 (2015).CrossRef P. J. García, V. M. González, J. C. Álvarez, et al., “A new predictive model of centerline segregation in continuous cast steel slabs by using multivariate adaptive regression splines approach,” Materials, 8, 3562–3583 (2015).CrossRef
28.
go back to reference P. García, E. García, J. Álvarez, et al., “A comparison of several machine learning techniques for the centerline segregation prediction in continuous cast steel slabs and evaluation of its performance,” J. Comput. Appl. Math., 330, 877–895 (2018).CrossRef P. García, E. García, J. Álvarez, et al., “A comparison of several machine learning techniques for the centerline segregation prediction in continuous cast steel slabs and evaluation of its performance,” J. Comput. Appl. Math., 330, 877–895 (2018).CrossRef
29.
go back to reference A. S. Normanton, B. Barber, A. Bell, et al., “Developments in online surface and internal quality forecasting of continuously cast semis,” Ironmak. Steelmak., 31, 376–382 (2004).CrossRef A. S. Normanton, B. Barber, A. Bell, et al., “Developments in online surface and internal quality forecasting of continuously cast semis,” Ironmak. Steelmak., 31, 376–382 (2004).CrossRef
30.
go back to reference H. Z. Chen, J. P. Yang, X. C. Lu, et al., “Quality prediction of the continuous casting bloom based on the extreme learning machine,” Chin. J. Eng., 40, 815–821 (2018). H. Z. Chen, J. P. Yang, X. C. Lu, et al., “Quality prediction of the continuous casting bloom based on the extreme learning machine,” Chin. J. Eng., 40, 815–821 (2018).
33.
go back to reference Y. Liu, T. Zhao, W. Ju, et al., “Materials discovery and design using machine learning,” J. Mater., 3, 159-177 (2017). Y. Liu, T. Zhao, W. Ju, et al., “Materials discovery and design using machine learning,” J. Mater., 3, 159-177 (2017).
34.
go back to reference C. Shen, C. Wang, X. Wei, et al., “Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel,” Acta Mater., 179, 201–214 (2019).CrossRef C. Shen, C. Wang, X. Wei, et al., “Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel,” Acta Mater., 179, 201–214 (2019).CrossRef
35.
go back to reference J. Majta, R. Kuziak, M. Pietrzyk, et al., “Use of the computer simulation to predict mechanical properties of C–Mn steel, after thermomechanical processing,” J. Mater. Process. Technol., 60, 581–588 (1996).CrossRef J. Majta, R. Kuziak, M. Pietrzyk, et al., “Use of the computer simulation to predict mechanical properties of C–Mn steel, after thermomechanical processing,” J. Mater. Process. Technol., 60, 581–588 (1996).CrossRef
36.
go back to reference H. N. Han, C. G. Lee, C.-S. Oh, et al., “A model for deformation behavior and mechanically induced martensitic transformation of metastable austenitic steel,” Acta Mater., 52, 5203–5214 (2004).CrossRef H. N. Han, C. G. Lee, C.-S. Oh, et al., “A model for deformation behavior and mechanically induced martensitic transformation of metastable austenitic steel,” Acta Mater., 52, 5203–5214 (2004).CrossRef
37.
go back to reference H. N. Han, J. K. Lee, H. J. Kim, et al., “A model for deformation, temperature and phase transformation behavior of steels on runout table in hot strip mill,” J. Mater. Process. Technol., 128, 216–225 (2002).CrossRef H. N. Han, J. K. Lee, H. J. Kim, et al., “A model for deformation, temperature and phase transformation behavior of steels on runout table in hot strip mill,” J. Mater. Process. Technol., 128, 216–225 (2002).CrossRef
38.
go back to reference E. Evin, J. Kepič, K. Buriková, et al., “The prediction of the mechanical properties for dual-phase high strength steel grades based on microstructure characteristics,” Metals, 8, 242 (2018).CrossRef E. Evin, J. Kepič, K. Buriková, et al., “The prediction of the mechanical properties for dual-phase high strength steel grades based on microstructure characteristics,” Metals, 8, 242 (2018).CrossRef
39.
go back to reference C. Şimşir and C. H. Gür, “An FEM based framework for simulation of thermal treatments: application to steel quenching,” Comput. Mater. Sci., 44, 588–600 (2008).CrossRef C. Şimşir and C. H. Gür, “An FEM based framework for simulation of thermal treatments: application to steel quenching,” Comput. Mater. Sci., 44, 588–600 (2008).CrossRef
40.
go back to reference A. Powar and P. Date, “Modeling of microstructure and mechanical properties of heat treated components by using artificial neural network,” Mater. Sci. Eng. A, 628, 89–97 (2015).CrossRef A. Powar and P. Date, “Modeling of microstructure and mechanical properties of heat treated components by using artificial neural network,” Mater. Sci. Eng. A, 628, 89–97 (2015).CrossRef
41.
go back to reference T. Jia, Z. Liu, H. Hu, et al., “The optimal design for the production of hot rolled strip with 'tight oxide scale’ by using multiobjective optimization,” ISIJ Int., 51, 1468–1473 (2011).CrossRef T. Jia, Z. Liu, H. Hu, et al., “The optimal design for the production of hot rolled strip with 'tight oxide scale’ by using multiobjective optimization,” ISIJ Int., 51, 1468–1473 (2011).CrossRef
42.
go back to reference M. S. Ozerdem and S. Kolukisa, “Artificial neural network approach to predict the mechanical properties of Cu–Sn–Pb–Zn–Ni cast alloys,” Mater. Des., 30, 764–769 (2009).CrossRef M. S. Ozerdem and S. Kolukisa, “Artificial neural network approach to predict the mechanical properties of Cu–Sn–Pb–Zn–Ni cast alloys,” Mater. Des., 30, 764–769 (2009).CrossRef
43.
go back to reference Z. Sterjovski, D. Nolan, K. R. Carpenter, et al., “Artificial neural networks for modelling the mechanical properties of steels in various applications,” J. Mater. Process. Technol., 170, 536–544 (2005).CrossRef Z. Sterjovski, D. Nolan, K. R. Carpenter, et al., “Artificial neural networks for modelling the mechanical properties of steels in various applications,” J. Mater. Process. Technol., 170, 536–544 (2005).CrossRef
44.
go back to reference R.-C. Hwang, Y.-J. Chen, and H.-C. Huang, “Artificial intelligent analyzer for mechanical properties of rolled steel bar by using neural networks,” Expert Syst. Appl., 37, 3136–3139 (2010).CrossRef R.-C. Hwang, Y.-J. Chen, and H.-C. Huang, “Artificial intelligent analyzer for mechanical properties of rolled steel bar by using neural networks,” Expert Syst. Appl., 37, 3136–3139 (2010).CrossRef
45.
go back to reference S. Lalam, P. K. Tiwari, S. Sahoo, et al., “Online prediction and monitoring of mechanical properties of industrial galvanised steel coils using neural networks,” Ironmak. Steelmak., 1–8 (2017). S. Lalam, P. K. Tiwari, S. Sahoo, et al., “Online prediction and monitoring of mechanical properties of industrial galvanised steel coils using neural networks,” Ironmak. Steelmak., 1–8 (2017).
46.
go back to reference S. B. Singh, H. K. D. H. Bhadeshia, D. J. C. Mackay, et al., “Neural network analysis of steel plate processing,” Ironmak. Steelmak., 25, 355–365 (1998). S. B. Singh, H. K. D. H. Bhadeshia, D. J. C. Mackay, et al., “Neural network analysis of steel plate processing,” Ironmak. Steelmak., 25, 355–365 (1998).
47.
go back to reference F. Pettersson, N. Chakraborti, and S. B. Singh, “Neural networks analysis of steel plate processing augmented by multi-objective genetic algorithms,” Steel Res. Int., 78, 890–898 (2007).CrossRef F. Pettersson, N. Chakraborti, and S. B. Singh, “Neural networks analysis of steel plate processing augmented by multi-objective genetic algorithms,” Steel Res. Int., 78, 890–898 (2007).CrossRef
48.
go back to reference F. Pettersson, N. Chakraborti, and H. Saxén, “A genetic algorithms based multi-objective neural net applied to noisy blast furnace data,” Appl. Soft Comput., 7, 387–397 (2007).CrossRef F. Pettersson, N. Chakraborti, and H. Saxén, “A genetic algorithms based multi-objective neural net applied to noisy blast furnace data,” Appl. Soft Comput., 7, 387–397 (2007).CrossRef
49.
go back to reference B. K. Giri, F. S. Pettersson, H. Saxén, et al., “Genetic programming evolved through bi-objective genetic algorithms applied to a blast furnace,” Mater. Manuf. Processes, 28, 776–782 (2013).CrossRef B. K. Giri, F. S. Pettersson, H. Saxén, et al., “Genetic programming evolved through bi-objective genetic algorithms applied to a blast furnace,” Mater. Manuf. Processes, 28, 776–782 (2013).CrossRef
50.
go back to reference B. Debanjana, P. P. Ranjan, D. P. Kumar, et al., “Datadriven biobjective genetic algorithms evonn applied to optimize dephosphorization process during secondary steel making operation for producing LPG (liquid petroleum gas cylinder) grade of steel,” Steel Res. Int., 89, 1800095 (2018).CrossRef B. Debanjana, P. P. Ranjan, D. P. Kumar, et al., “Datadriven biobjective genetic algorithms evonn applied to optimize dephosphorization process during secondary steel making operation for producing LPG (liquid petroleum gas cylinder) grade of steel,” Steel Res. Int., 89, 1800095 (2018).CrossRef
51.
go back to reference S. Pal and C. Halder, “Optimization of phosphorous in steel produced by basic oxygen steel making process using multi-objective evolutionary and genetic algorithms,” Steel Res. Int., 88, 1600193 (2017).CrossRef S. Pal and C. Halder, “Optimization of phosphorous in steel produced by basic oxygen steel making process using multi-objective evolutionary and genetic algorithms,” Steel Res. Int., 88, 1600193 (2017).CrossRef
52.
go back to reference T. Chugh, N. Chakraborti, K. Sindhya, et al., “A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem,” Mater. Manuf. Processes, 32, 1172–1178 (2017).CrossRef T. Chugh, N. Chakraborti, K. Sindhya, et al., “A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem,” Mater. Manuf. Processes, 32, 1172–1178 (2017).CrossRef
53.
go back to reference B. K. Mahanta and N. Chakraborti, “Evolutionary data driven modeling and multi objective optimization of noisy data set in blast furnace iron making process,” Steel Res. Int., 89, 1800121 (2018).CrossRef B. K. Mahanta and N. Chakraborti, “Evolutionary data driven modeling and multi objective optimization of noisy data set in blast furnace iron making process,” Steel Res. Int., 89, 1800121 (2018).CrossRef
54.
go back to reference H. Peters, A. Ebel, M. Holzknecht, et al., “Industrial data mining in steel industry,” Journees Siderurgiques Internationales, 7, 53–68 (2012). H. Peters, A. Ebel, M. Holzknecht, et al., “Industrial data mining in steel industry,” Journees Siderurgiques Internationales, 7, 53–68 (2012).
55.
go back to reference W. N. L. Browne, “The development of an industrial learning classifier system for data-mining in a steel hot strip mill,” Appl. Learning Classifier Systems, 150, 223–259 (2004).CrossRef W. N. L. Browne, “The development of an industrial learning classifier system for data-mining in a steel hot strip mill,” Appl. Learning Classifier Systems, 150, 223–259 (2004).CrossRef
56.
go back to reference A. Agrawal, P. D. Deshpande, A. Cecen, et al., “Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters,” Integrating Mater. Manuf. Innovation, 3, 90–108 (2014).CrossRef A. Agrawal, P. D. Deshpande, A. Cecen, et al., “Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters,” Integrating Mater. Manuf. Innovation, 3, 90–108 (2014).CrossRef
57.
go back to reference R. Jha, N. Chakraborti, D. R. Diercks, et al., “Optimal mean radius and volume fraction of the nanocrystalline phase in softmagnetic alloys: a combined machine learning and calphad approach,” Comput. Mater. Sci., 150, 202–211 (2018).CrossRef R. Jha, N. Chakraborti, D. R. Diercks, et al., “Optimal mean radius and volume fraction of the nanocrystalline phase in softmagnetic alloys: a combined machine learning and calphad approach,” Comput. Mater. Sci., 150, 202–211 (2018).CrossRef
65.
go back to reference G. X. Liu, L. N. Jia, B. Kong, et al., “Artificial neural network application to microstructure design of Nb–Si alloy to improve ultimate tensile strength,” Mater. Sci. Eng. A, 707, 452–458 (2017).CrossRef G. X. Liu, L. N. Jia, B. Kong, et al., “Artificial neural network application to microstructure design of Nb–Si alloy to improve ultimate tensile strength,” Mater. Sci. Eng. A, 707, 452–458 (2017).CrossRef
66.
go back to reference X. Y. Sui and Z. M. Lv, “Prediction of the mechanical properties of hot rolling products by using attribute reduction ELM,” Int. J. Adv. Manuf. Technol., 85, No. 5, 1395–1403 (2016).CrossRef X. Y. Sui and Z. M. Lv, “Prediction of the mechanical properties of hot rolling products by using attribute reduction ELM,” Int. J. Adv. Manuf. Technol., 85, No. 5, 1395–1403 (2016).CrossRef
67.
go back to reference L. Ciripova, E. Hryha, E. Dudrova, et al., “Prediction of mechanical properties of Fe–Cr–Mo sintered steel in relationship with microstructure,” Mater. Des., 35, 619–625 (2012).CrossRef L. Ciripova, E. Hryha, E. Dudrova, et al., “Prediction of mechanical properties of Fe–Cr–Mo sintered steel in relationship with microstructure,” Mater. Des., 35, 619–625 (2012).CrossRef
68.
go back to reference C. Z. Zhang, B. M. Gong, C. Y. Deng, et al., “Computational prediction of mechanical properties of a C–Mn weld metal based on the microstructures and micromechanical properties,” Mater. Sci. Eng. A, 685, 310–316 (2017).CrossRef C. Z. Zhang, B. M. Gong, C. Y. Deng, et al., “Computational prediction of mechanical properties of a C–Mn weld metal based on the microstructures and micromechanical properties,” Mater. Sci. Eng. A, 685, 310–316 (2017).CrossRef
69.
go back to reference A. Kumar, D. Chakrabarti, and N. Chakraborti, “Datadriven pareto optimization for microalloyed steels using genetic algorithms,” Steel Res. Int., 83, No. 2, 169-174 (2012).CrossRef A. Kumar, D. Chakrabarti, and N. Chakraborti, “Datadriven pareto optimization for microalloyed steels using genetic algorithms,” Steel Res. Int., 83, No. 2, 169-174 (2012).CrossRef
70.
go back to reference R. Jha, F. Pettersson, G. S. Dulikravich, et al., “Evolutionary design of Nickel-based superalloys using datadriven genetic algorithms and related strategies,” Mater. Manuf. Process., 30, No. 4, 488–510 (2015).CrossRef R. Jha, F. Pettersson, G. S. Dulikravich, et al., “Evolutionary design of Nickel-based superalloys using datadriven genetic algorithms and related strategies,” Mater. Manuf. Process., 30, No. 4, 488–510 (2015).CrossRef
71.
go back to reference S. Ganguly, S. Datta, and N. Chakraborti, “Genetic algorithms in optimization of strength and ductility of low-carbon steels,” Mater. Manuf. Process., 22, No. 5, 650–658 (2007).CrossRef S. Ganguly, S. Datta, and N. Chakraborti, “Genetic algorithms in optimization of strength and ductility of low-carbon steels,” Mater. Manuf. Process., 22, No. 5, 650–658 (2007).CrossRef
72.
go back to reference G. X. Liu, L. N. Jia, B. Kong, et al., “Artificial neural network application to study quantitative relationship between silicide and fracture toughness of Nb–Si alloys,” Mater. Des., 129, 210–218 (2017).CrossRef G. X. Liu, L. N. Jia, B. Kong, et al., “Artificial neural network application to study quantitative relationship between silicide and fracture toughness of Nb–Si alloys,” Mater. Des., 129, 210–218 (2017).CrossRef
74.
go back to reference Atlas of Isothermal Transformation and Cooling Transformation Diagrams, American Society of Metals (1977). Atlas of Isothermal Transformation and Cooling Transformation Diagrams, American Society of Metals (1977).
75.
go back to reference J. Trzaska and L. A. Dobrzański, “Modelling of CCT diagrams for engineering and constructional steels,” J. Mater. Process. Technol., 192, 504–510 (2007).CrossRef J. Trzaska and L. A. Dobrzański, “Modelling of CCT diagrams for engineering and constructional steels,” J. Mater. Process. Technol., 192, 504–510 (2007).CrossRef
76.
go back to reference Y. Wei, X. U. Wei-hong, L. Ya-xiu, et al., “Effect of chromium on CCT diagrams of novel air-cooled bainite steels analyzed by neural network,” J. Iron. Steel Res. Int., 14, 39-42 (2007).CrossRef Y. Wei, X. U. Wei-hong, L. Ya-xiu, et al., “Effect of chromium on CCT diagrams of novel air-cooled bainite steels analyzed by neural network,” J. Iron. Steel Res. Int., 14, 39-42 (2007).CrossRef
77.
go back to reference S. Chakraborty, P. P. Chattopadhyay, S. K. Ghosh, et al., “Incorporation of prior knowledge in neural network model for continuous cooling of steel using genetic algorithm,” Appl. Soft Comput., 58, 297–306 (2017).CrossRef S. Chakraborty, P. P. Chattopadhyay, S. K. Ghosh, et al., “Incorporation of prior knowledge in neural network model for continuous cooling of steel using genetic algorithm,” Appl. Soft Comput., 58, 297–306 (2017).CrossRef
78.
go back to reference X. Geng, H. Wang, W. Xue, et al., “Modeling of CCT diagrams for tool steels using different machine learning techniques,” Computational Mater. Sci., 171, 109235 (2020).CrossRef X. Geng, H. Wang, W. Xue, et al., “Modeling of CCT diagrams for tool steels using different machine learning techniques,” Computational Mater. Sci., 171, 109235 (2020).CrossRef
79.
go back to reference S. K. Ghosh, P. P. Chattopadhyay, A. Haldar, et al., “Design of the directly air-cooled pearlite-free multiphase steel from CCT diagrams developed using ANN and dilatometric methods,” ISIJ Int., 48, No. 5, 649–657 (2008).CrossRef S. K. Ghosh, P. P. Chattopadhyay, A. Haldar, et al., “Design of the directly air-cooled pearlite-free multiphase steel from CCT diagrams developed using ANN and dilatometric methods,” ISIJ Int., 48, No. 5, 649–657 (2008).CrossRef
80.
go back to reference Z.-L. Wang, T. Ogawa, and Y. Adachi, “Properties-to-microstructure-to-processing inverse analysis for steels via machine learning,” ISIJ Int., 59, No. 9, 1691–1694 (2019).CrossRef Z.-L. Wang, T. Ogawa, and Y. Adachi, “Properties-to-microstructure-to-processing inverse analysis for steels via machine learning,” ISIJ Int., 59, No. 9, 1691–1694 (2019).CrossRef
82.
go back to reference Z. Zhu, Y. Liang, and J. Zou, “Modeling and composition design of low-alloy steel’s mechanical properties based on neural networks and genetic algorithms,” Materials, 13, No. 23, 1–23 (2020).CrossRef Z. Zhu, Y. Liang, and J. Zou, “Modeling and composition design of low-alloy steel’s mechanical properties based on neural networks and genetic algorithms,” Materials, 13, No. 23, 1–23 (2020).CrossRef
83.
go back to reference V. Colla, S. Cateni, A. Maddaloni, et al., “A modular machine-learning-based approach to improve tensile properties uniformity along hot dip galvanized steel strips for automotive applications,” Metals, 10, No. 7, 1–23 (2020).CrossRef V. Colla, S. Cateni, A. Maddaloni, et al., “A modular machine-learning-based approach to improve tensile properties uniformity along hot dip galvanized steel strips for automotive applications,” Metals, 10, No. 7, 1–23 (2020).CrossRef
86.
go back to reference B. Nenchev, C. Panwisawas, X. Yang, et al., “Metallurgical data science for steel industry: a case study on basic oxygen furnace,” Steel Res. Int., 2100813, 1–11 (2022). B. Nenchev, C. Panwisawas, X. Yang, et al., “Metallurgical data science for steel industry: a case study on basic oxygen furnace,” Steel Res. Int., 2100813, 1–11 (2022).
87.
go back to reference M. Zhang, Y. Chen, L. Xu, et al., “A novel optic sensor for real-time metal analysis in the bof steelmaking process,” Advanced Materials Research, 156–157, 1594–1597 (2011). M. Zhang, Y. Chen, L. Xu, et al., “A novel optic sensor for real-time metal analysis in the bof steelmaking process,” Advanced Materials Research, 156–157, 1594–1597 (2011).
88.
go back to reference M. Zhang, Y. Chen, L. Xu, et al., “New optic sensor for real-time bath temperature measurement in a BOF,” Advanced Materials Research, 181–182, 642–646 (2011). M. Zhang, Y. Chen, L. Xu, et al., “New optic sensor for real-time bath temperature measurement in a BOF,” Advanced Materials Research, 181–182, 642–646 (2011).
89.
go back to reference J. Brandenburger, V. Colla, G. Nastasi, et al., “Big data solution for quality monitoring and improvement on flat steel production,” IFAC-PapersOnLine, 49–20, 55–60 (2016).CrossRef J. Brandenburger, V. Colla, G. Nastasi, et al., “Big data solution for quality monitoring and improvement on flat steel production,” IFAC-PapersOnLine, 49–20, 55–60 (2016).CrossRef
90.
go back to reference S. Gellrich, M.-A. Filz, A.-S. Wilde, et al., “Deep transfer learning for improved product quality prediction: A case study of aluminum gravity die casting,” Procedia CIRP, 104, 912–917 (2021).CrossRef S. Gellrich, M.-A. Filz, A.-S. Wilde, et al., “Deep transfer learning for improved product quality prediction: A case study of aluminum gravity die casting,” Procedia CIRP, 104, 912–917 (2021).CrossRef
92.
go back to reference A. Kazama, K. Kawamura, K. Tsuda, et al., “Development of utilization of digital data in JFE steel,” JFE Technical Report, No. 26, 8 (2021). A. Kazama, K. Kawamura, K. Tsuda, et al., “Development of utilization of digital data in JFE steel,” JFE Technical Report, No. 26, 8 (2021).
93.
go back to reference J. Krumeich, J. Schimmelpfennig, and S. Jacobi, “Advanced planning and control of manufacturing processes in steel industry through big data analytics,” in: Proc. of the IEEE Intern. Conf. on Big Data (2014), pp. 16–24. J. Krumeich, J. Schimmelpfennig, and S. Jacobi, “Advanced planning and control of manufacturing processes in steel industry through big data analytics,” in: Proc. of the IEEE Intern. Conf. on Big Data (2014), pp. 16–24.
94.
go back to reference S. M. Zanoli, C. Pepe, E. Moscoloni, et al., “Data analysis and modelling of billets features in steel industry,” Sensors, 22, No. 7333, 1–20 (2022). S. M. Zanoli, C. Pepe, E. Moscoloni, et al., “Data analysis and modelling of billets features in steel industry,” Sensors, 22, No. 7333, 1–20 (2022).
95.
96.
97.
go back to reference A. Stoianova and N. Vasilyeva, “Production process data as a tool for digital transformation of metallurgical companies,” in: Proc. of the 14th Intern. Sci. Conf. “INTERAGROMASH 2021” (2021), pp. 780–787. A. Stoianova and N. Vasilyeva, “Production process data as a tool for digital transformation of metallurgical companies,” in: Proc. of the 14th Intern. Sci. Conf. “INTERAGROMASH 2021” (2021), pp. 780–787.
98.
go back to reference F. Abbassi, T. Belhadj, S. Mistou, et al., “Parameter identification of a mechanical ductile damage using artificial neural networks in sheet metal forming,” Materials and Design, 45, 605–615 (2013).CrossRef F. Abbassi, T. Belhadj, S. Mistou, et al., “Parameter identification of a mechanical ductile damage using artificial neural networks in sheet metal forming,” Materials and Design, 45, 605–615 (2013).CrossRef
99.
go back to reference M. Bartolomei, A. Kliuev, A. Rogozhnikov, et al., “Classification of the type of hardened steel destruction using a deep learn neural network,” in: DSIC 2019, AISC 1114 (2020), pp. 513–521. M. Bartolomei, A. Kliuev, A. Rogozhnikov, et al., “Classification of the type of hardened steel destruction using a deep learn neural network,” in: DSIC 2019, AISC 1114 (2020), pp. 513–521.
100.
go back to reference A. Panda, R. Naskar, and S. Pal, “Deep learning approach for segmentation of plain carbon steel microstructure images,” IET Image Processing, 13, No. 9, 1516–1524 (2019).CrossRef A. Panda, R. Naskar, and S. Pal, “Deep learning approach for segmentation of plain carbon steel microstructure images,” IET Image Processing, 13, No. 9, 1516–1524 (2019).CrossRef
101.
go back to reference L. Xiong, J. Ning, and Y. Dong, “Pollution reduction effect of the digital transformation of heavy metal enterprises under the agglomeration effect,” J. Cleaner Production, 330, 129864 (2022).CrossRef L. Xiong, J. Ning, and Y. Dong, “Pollution reduction effect of the digital transformation of heavy metal enterprises under the agglomeration effect,” J. Cleaner Production, 330, 129864 (2022).CrossRef
102.
go back to reference A. V. Muntin, M. N. Shamshin, A. G. Zinyagin, et al., “Digitalization is most important tool for improving metallurgical technologies,” Metallurgist, No. 9, 31–43 (2022).CrossRef A. V. Muntin, M. N. Shamshin, A. G. Zinyagin, et al., “Digitalization is most important tool for improving metallurgical technologies,” Metallurgist, No. 9, 31–43 (2022).CrossRef
103.
go back to reference A. E. Sevidov, A. V. Muntin, and A. G. Kolesnikov, “Modeling of mechanical wear of work rolls in a wide-strip hot rolling mill using machine learning methods,” Chernye Metally, No. 11, 22–27 (2022).CrossRef A. E. Sevidov, A. V. Muntin, and A. G. Kolesnikov, “Modeling of mechanical wear of work rolls in a wide-strip hot rolling mill using machine learning methods,” Chernye Metally, No. 11, 22–27 (2022).CrossRef
104.
go back to reference A. E. Sevidov, A. V. Muntin, and A. V. Rumyantsev, “Investigation of the friction coefficient in the steady-state process of continuous hot rolling of steel strips under the industrial mill 1950 conditions,” Chernye Metally, No. 9, 29–35 (2021).CrossRef A. E. Sevidov, A. V. Muntin, and A. V. Rumyantsev, “Investigation of the friction coefficient in the steady-state process of continuous hot rolling of steel strips under the industrial mill 1950 conditions,” Chernye Metally, No. 9, 29–35 (2021).CrossRef
Metadata
Title
Artificial Intelligence and Machine Learning In Metallurgy. Part 2. Application Examples
Authors
P. Yu. Zhikharev
A. V. Muntin
D. A. Brayko
M. O. Kryuchkova
Publication date
13-03-2024
Publisher
Springer US
Published in
Metallurgist / Issue 9-10/2024
Print ISSN: 0026-0894
Electronic ISSN: 1573-8892
DOI
https://doi.org/10.1007/s11015-024-01648-y

Other articles of this Issue 9-10/2024

Metallurgist 9-10/2024 Go to the issue

Premium Partners