PLC realization and algorithm research of cold rolling mill thickness control system based on neural network

Zhaoxia Zhang

doi:10.1117/12.2644683

10 November 2022 PLC realization and algorithm research of cold rolling mill thickness control system based on neural network

Zhaoxia Zhang

Author Affiliations +

Proceedings Volume 12301, 6th International Conference on Mechatronics and Intelligent Robotics (ICMIR2022); 123012R (2022) https://doi.org/10.1117/12.2644683
Event: 6th International Conference on Mechatronics and Intelligent Robotics, 2022, Kunming, China

Abstract

Thickness accuracy is one of the important quality indexes of strip products. With the development of social economy, the quality requirements of strip products are higher and higher, and the requirements for precision are also higher and higher. Many original production lines have been unable to meet the requirements, the existing unqualified production line transformation becomes very urgent. A control scheme of thickness control system of cold rolling mill based on Siemens S7-400PLC is presented. How to improve in a stable in the process of rolling steel strip thickness control precision, and improve the yield of plate and strip, is today a most popular courses in the field of the strip thickness control, aluminum plate is in hot rolling process of cold rolling mill or coil as wool roll casting process, a multiple passes, the rolling rolling out the qualified finished product coil, A rolling mill that provides billets for sheet shearing, stretching and bending straightening and aluminum foil deep processing. Artificial neural network technology has been successfully applied in many fields of automation system, such as system modeling and identification, robot control, system pattern discrimination, system fault diagnosis and so on. In the lag link of AGC system, the self-learning ability of the neural network is used to adjust the integral controller parameter value online. The weight of the neural network is corresponding to the integral parameter value, and the integral parameter can be adjusted according to the dynamic characteristics of the controlled system, thus improving the adaptive ability of the regulator.

Citation Download Citation

Zhaoxia Zhang "PLC realization and algorithm research of cold rolling mill thickness control system based on neural network", Proc. SPIE 12301, 6th International Conference on Mechatronics and Intelligent Robotics (ICMIR2022), 123012R (10 November 2022); https://doi.org/10.1117/12.2644683

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KEYWORDS

Control systems

Photonic integrated circuits

Neural networks

Servomechanisms

Sensors

Analog electronics

Evolutionary algorithms

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