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Optimization of work roll bending model in unsteady process of tandem cold rolling based on CF-PSO-SVM |
SONG Jun1,2, REN Ting-zhi1, WANG Kui-yue2, WANG Jun-sheng2 |
1. Education Ministry Engineering Research Center of Rolling Equipment and Complete Technology, Yanshan University, Qinhuangdao 066004, Hebei, China; 2. Ansteel Beijing Research Institute, Ansteel Group, Beijing 102211, China |
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Abstract Flatness quality is an important technical quality specifications of cold rolled strip. At the same time,work roll bending is one of the most effective control methods to improve the flatness quality of cold rolled strip. The strip flatness control precision of tandem cold mill can reach a high level in the process of high speed and stable rolling,but the effect of shape control is not ideal in the process of unsteady rolling,this has also become a negative factor restricting the quality of cold rolled strip. In order to improve the control precision of flatness in the unsteady process of acceleration and deceleration,the principle of bending force setting in tandem cold rolling was studied,combined with the intelligent algorithms and the actual rolling data samples including the strip thickness at the entry and outlet,the tension between the stands,the rolling speed,the middle roll shifting,the strip width,the roll inclination and the rolling force,a bending force prediction strategy based on the support vector machine theory of which parameters were optimized by the particle swarm optimization algorithm was proposed. The principle of support vector machine (SVM) theory and particle swarm optimization (PSO) algorithm was studied. In order to improve the parameters optimization ability,the concepts of compression factor were introduced in the PSO algorithm. Moreover,the PauTa criterion and five-point three-time smoothing method were used to process the relevant rolling data,and then the performance of the prediction strategies was compared by the evaluation indexes,such as mean square error and mean absolute error. The results show that the proposed algorithm was verified with good predictive performance and excellent generalization ability,at the same time,according to the actual production data sample,the compensation model of bending force gap based on rolling speed and roll elasticity coefficient was regressed,and the validity of the model was verified,the model reduced the load of the flatness control system and improved the flatness control precision in unsteady rolling process,the quality pass rate of the head and tail of the product was increased by 5.1%.
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Received: 18 March 2021
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