Intelligent assignation strategy of collaborative optimization for flatness control

  • Zhu-wen Yan
  • Bao-sheng Wang
  • He-nan Bu
  • Dian-hua Zhang
Technical Paper


Under the background of industry 4.0, advanced strip control process is a central part of rolling intelligent manufacturing. In the mainstream rolling control process, the work roll bending and the intermediate roll bending are turned on at the same time. Both stepwise methods and alternative methods are conducted by sacrificing adjustment ability. In this paper, the dimension of influencing factors is increased by considering adjustment direction as constraint operator. In the rolling control process, a new intelligent assignation strategy of collaborative optimization based on artificial neural networks and Topkis–Veinott has been proposed. In AINTV collaborative optimization, the thought patterns of searching and the thought patterns of learning are combined. Five field test experiments are conducted and the flatness in different rolling stages and in different strip area is analyzed.


Rolling control process Artificial neural networks Intelligent manufacturing Collaborative optimization Calculation of bending force 



This study is financially supported by the National Natural Science Foundation of China, China (No.: 51074052).

Compliance with ethical standards

Conflict of interest

The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.


  1. 1.
    Chudasama M, Raval H (2013) An approximate bending force prediction for 3-roller conical bending process. Int J Mater Form 6:303–314CrossRefGoogle Scholar
  2. 2.
    Pour HSS, Beheshti HK, Alizadeh Y, Poursina M (2014) Calculation of work roll initial crown based on desired strip profile in hot rolling. Neural Comput Appl 24:1123–1133CrossRefGoogle Scholar
  3. 3.
    Wang XD, Li F, Li BH, Dong LJ, Zhang BH (2012) Design and application of an optimum backup roll contour configured with CVC work roll in hot strip mill. ISIJ Int 52:1637–1643CrossRefGoogle Scholar
  4. 4.
    Li YL, Cao JG, Yang GH, Wen D, Zhou YZ, Ma HH (2015) ASR bending force mathematical model for the same width strip rolling campaigns in hot rolling. Steel Res Int 86:567–575CrossRefGoogle Scholar
  5. 5.
    Cao JG, Xu XZ, Zhang J, Song MQ, Gong GL, Zeng W (2011) Preset model of bending force for 6-high reversing cold rolling mill based on genetic algorithm. J Central South Univ Technol 18(5):1487–1492CrossRefGoogle Scholar
  6. 6.
    Chudasama MK, Raval HK (2014) Bending force prediction for dynamic roll-bending during 3-roller conical bending process. J Manuf Process 16:284–295CrossRefGoogle Scholar
  7. 7.
    Wang XD, Li F, Wang L, Zhang XL, Dong LJ (2012) Development and application of roll contour configuration in temper rolling mill for hot rolled thin gauge steel strip. Ironmaking Steelmaking 39:163–170CrossRefGoogle Scholar
  8. 8.
    Shen GX, Zheng YJ, Li M (2013) Development of statically determinate plate rolling mills that maintain the rolls parallel. J Manuf Sci Eng Trans ASME 135:031014-1–031014-8CrossRefGoogle Scholar
  9. 9.
    Zeng J, Liu ZH, Champliaud H (2008) FEM dynamic simulation and analysis of the roll-bending process for forming a conical tube. J Mater Process Technol 198:330–343CrossRefGoogle Scholar
  10. 10.
    Park JS, Na DH, Yang Z, Hur SM, Chung SH, Lee Y (2016) Application of neural networks to minimize front end bending of material in plate rolling process. Proceed Inst Mech Eng Part B J Eng Manuf 230:629–642CrossRefGoogle Scholar
  11. 11.
    Linghu KZ, Jiang ZY, Zhao JW, Li F, Wei DB, Xu JZ, Zhang XM, Zhao XM (2014) 3D FEM analysis of strip shape during multi-pass rolling in a 6-high CVC cold rolling mill. Int J Adv Manuf Technol 74:1733–1745CrossRefGoogle Scholar
  12. 12.
    Alimoradi H, Shams M (2017) Optimization of subcooled flow boiling in a vertical pipe by using artificial neural network and multi objective genetic algorithm. Appl Therm Eng 111:1039–1051CrossRefGoogle Scholar
  13. 13.
    Pakdaman M, Ahmadian A, Effati S, Salahshour S, Baleanu D (2017) Solving differential equations of fractional order using an optimization technique based on training artificial neural network. Appl Math Comput 293:81–95MathSciNetGoogle Scholar
  14. 14.
    Yadav N, Yadav A, Kumar M, Kim JH (2017) An efficient algorithm based on artificial neural networks and particle swarm optimization for solution of nonlinear Troesch’s problem. Neural Comput Appl 28:171–178CrossRefGoogle Scholar
  15. 15.
    Razin MRG, Voosoghi B (2016) Wavelet neural networks-using particle swarm optimization training in modeling regional ionospheric total electron content. J Atmos Solar Terr Phys 149:21–30CrossRefGoogle Scholar
  16. 16.
    Borah T, Bhattacharjya RK (2016) Development of an improved pollution source identification model using numerical and ANN based simulation-optimization model. Water Resour Manage 30:5163–5176CrossRefGoogle Scholar
  17. 17.
    Avci H, Kumlutas D, Ozer O, Ozsen M (2016) Optimisation of the design parameters of a domestic refrigerator using CFD and artificial neural networks. Int J Refrig Revue Int Du Froid 67:227–238CrossRefGoogle Scholar
  18. 18.
    Song XZ, Peng C, Li GS, He ZG, Wang HZ (2016) Optimization of operation parameters for helical flow cleanout with supercritical CO2 in horizontal wells using back-propagation artificial neural network. PLoS ONE 11(6):1932–6203Google Scholar
  19. 19.
    Li B, Gu CW, Li XT, Liu TQ (2016) Numerical optimization for stator vane settings of multi-stage compressors based on neural networks and genetic algorithms. Aerosp Sci Technol 52:81–94CrossRefGoogle Scholar
  20. 20.
    Li B, Gu CW (2016) Numerical optimization of a highly loaded compressor in semi-closed cycles using neural networks and genetic algorithms. Greenhouse Gases-Sci Technol 6:232–250CrossRefGoogle Scholar
  21. 21.
    Hu L, Qin LH, Mao K, Chen WY, Fu X (2016) Optimization of neural network by genetic algorithm for flowrate determination in multipath ultrasonic gas flowmeter. IEEE Sens J 16:1158–1167CrossRefGoogle Scholar
  22. 22.
    Shen CY, Wang LX, Li Q (2007) Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method. J Mater Process Technol 183:412–418CrossRefGoogle Scholar
  23. 23.
    Abouhamze M, Shakeri M (2007) Multi-objective stacking sequence optimization of laminated cylindrical panels using a genetic algorithm and neural networks. Compos Struct 81:253–263CrossRefGoogle Scholar
  24. 24.
    Elsayed K, Lacor C (2012) Modeling and Pareto optimization of gas cyclone separator performance using RBF type artificial neural networks and genetic algorithms. Powder Technol 217:84–99CrossRefGoogle Scholar
  25. 25.
    Istadi I, Amin NAS (2007) Modelling and optimization of catalytic-dielectric barrier discharge plasma reactor for methane and carbon dioxide conversion using hybrid artificial neural network—genetic algorithm technique. Chem Eng Sci 62:6568–6581CrossRefGoogle Scholar
  26. 26.
    Hsieh KL, Tong LI (2001) Optimization of multiple quality responses involving qualitative and quantitative characteristics in IC manufacturing using neural networks. Comput Ind 46:1–12CrossRefGoogle Scholar
  27. 27.
    Elsayed K, Lacor C (2013) CFD modeling and multi-objective optimization of cyclone geometry using desirability function, artificial neural networks and genetic algorithms. Appl Math Model 37:5680–5704CrossRefGoogle Scholar
  28. 28.
    Hugget A, Sebastian P, Nadeau JP (1999) Global optimization of a dryer by using neural networks and genetic algorithms. AIChE J 45:1227–1238CrossRefGoogle Scholar
  29. 29.
    Jamshidi M, Ghaedi M, Dashtian K, Ghaedi AM, Hajati S, Goudarzi A, Alipanahpour E (2016) Highly efficient simultaneous ultrasonic assisted adsorption of brilliant green and eosin B onto ZnS nanoparticles loaded activated carbon: artificial neural network modeling and central composite design optimization. Spectrochim Acta Part A Mol Biomol Spectrosc 153:257–267CrossRefGoogle Scholar
  30. 30.
    Cho JR, Shin SW (2004) Material composition optimization for heat-resisting FGMs by artificial neural network. Compos Part A Appl Sci Manuf 35:585–594CrossRefGoogle Scholar
  31. 31.
    Shi HZ, Gao YH, Wang XC (2010) Optimization of injection molding process parameters using integrated artificial neural network model and expected improvement function method. Int J Adv Manuf Technol 48:955–962CrossRefGoogle Scholar
  32. 32.
    Bu HN, Yan ZW, Zhang DH, Chen SZ (2016) Rolling-schedule multi-objective optimization based on influence function for thin-gauge steel strip in tandem cold rolling. Sci Iran 23:2663–2672Google Scholar
  33. 33.
    Yan ZW, Bu HN, Zhang DH (2016) Optimization and innovative modification of a model used to determine the amount of adjustment of an actuator for flatness control. Metallurgist 59:795–804CrossRefGoogle Scholar
  34. 34.
    Birge JR, Qi L, Wei Z (2000) A variant of the Topkis–Veinott method for solving inequality constrained optimization problems. Appl Math Optim 41:309–330MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    Kostreva MM, Chen X (2000) A superlinearly convergent method of feasible directions. Appl Math Comput 116:231–244MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    Wang PF, Zhang DH, Li X, Zhang WX (2011) Research and application of dynamic substitution control of actuators in flatness control of cold rolling mill. Steel Res Int 82(4):379–387CrossRefGoogle Scholar
  37. 37.
    Zhang XL, Zhao L, Zang JY, Fan HM, Cheng L (2014) Flatness intelligent control based on T-S cloud inference neural network. ISIJ Int 54(11):2608–2617CrossRefGoogle Scholar
  38. 38.
    Wang QL, Sun J, Liu YM, Wang PF, Zhang DH (2017) Analysis of symmetrical flatness actuator efficiencies for UCM cold rolling mill by 3D elastic–plastic FEM. Int J Adv Manuf Technol 10:1–19Google Scholar

Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2018

Authors and Affiliations

  • Zhu-wen Yan
    • 1
  • Bao-sheng Wang
    • 1
  • He-nan Bu
    • 2
  • Dian-hua Zhang
    • 2
  1. 1.Jiangsu Provincial Engineering Laboratory of Intelligent Manufacturing Equipment, Research Department of Intelligent Manufacturing EquipmentNanjing Institute of TechnologyNanjingPeople’s Republic of China
  2. 2.State Key Laboratory of Rolling and AutomationNortheastern UniversityShenyangPeople’s Republic of China

Personalised recommendations