Intelligent assignation strategy of collaborative optimization for flatness control
- 92 Downloads
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.
KeywordsRolling 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.
- 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
- 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
- 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
- 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