Development of optimization technology for disc shear process parameters of hot-dip galvanizing line
BAI Zhen-hua1,2, LIN Wei1, WANG Wei1, ZHANG Wen-jun1, LI Xue-tong1,2
1. National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, Yanshan University, Qinhuangdao 066004, Hebei, China; 2. State Key Laboratory of Metastable Materials Science and Technology, Yanshan University, Qinhuangdao 066004, Hebei, China
Abstract：In order to study the main cutting edge quality problems such as the disc shear equipment failure due to improper setting of the cutting process parameters and the occurrence of burrs, burrs and unbalanced cut-off ratio at the edge of the strip due to improper setting of the cutting process parameters during the work. This article takes the disc shear equipment of a hot-dip galvanizing line of a steel plant as the research object, and conducts research on the equipment itself and process parameters. Through the analysis of the factors that affect the quality of the strip edges and the correlation of the input variables of the BP neural network, the four factors of the front tension, the amount of clearance, the amount of overlap, and the amount of trimming are selected as variables and the hot-rolled strip steel disc is analyzed. Based on the shearing mechanism of the blades, combined with the technological characteristics of the disc shearing equipment and the relevant parameters in the cutting process, with the goal of preventing equipment failures and improving the quality of the current edge, the different indicators of the strip edge are carried out correspondingly quantify, put forward the strip quality evaluation function, and use the calculation result as the output layer of BP neural network. On this basis, a model based on PSO-BP neural network was established, and a set of hot-dip galvanizing line trimming process optimization technology was developed. The data source, processing method and the setting method of the parameters of the PSO-BP neural network are also given. The technology trains the BP neural network based on the tens of thousands of key production process parameters of disc shears collected on site, and fits the nonlinear function on this basis. Finally, the particle swarm algorithm PSO is used to analyze the better disc shear clearance. Process parameters such as the amount of overlap and the front and rear tension have caused the trimming quality problem at the edge of the strip after the disc shears. The ratio of the total trimming kilometers is significantly lower than before, which effectively improves the trimming quality of the strip. Lays a foundation for the improvement of unit production efficiency, and at the same time creates better economic benefits for the enterprise.
白振华, 林威, 王伟, 张文军, 李学通. 热镀锌机组圆盘剪工艺参数优化技术的开发[J]. 钢铁, 2022, 57(1): 159-166.
BAI Zhen-hua, LIN Wei, WANG Wei, ZHANG Wen-jun, LI Xue-tong. Development of optimization technology for disc shear process parameters of hot-dip galvanizing line[J]. Iron and Steel, 2022, 57(1): 159-166.
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2022, Vol. 57 
