Self-Learning of Rolling Force in “1+4” Aluminum Hot Tandem Rolling

Abstract

Abstract:

The predication accuracy of rolling force is an important factor affecting the accuracy of plate thickness and crown in aluminum hot tandem finishing rolling process.To improve the prediction accuracy of rolling force,a method of rolling force model self-learning was established based on lots of actual measured rolling data of aluminum alloy from one factory of aluminum hot tandem rolling.A BFO algorithm was applied to optimize the gain coefficient of the self-learning method.

Key words: aluminum hot tandem rolling, rolling force, mathematical model, self-learning, bacteria foraging optimization(BFO) algorithm

摘要：

在铝热连轧精轧生产过程中,轧制力的预报精度直接影响板厚和板凸度控制精度。针对河南某1+4铝热连轧机现场轧制力预报精度较低的问题,根据现场采集的大量轧制数据,建立了轧制力模型自学习算法,并用细菌觅食优化算法对自学习中的增益系数进行了优化,提高了轧制力预报精度。

关键词: 铝热连轧, 轧制力, 数学模型, 自学习, 细菌觅食优化算法

CLC Number:

TG335.13

Yang Jingming, Ma Fengyan, Che Haijun, Du Nan, . Self-Learning of Rolling Force in “1+4” Aluminum Hot Tandem Rolling[J]. China Mechanical Engineering, 2013, 24(24): 3300-3303.

杨景明, 马凤艳, 车海军, 杜楠, . “1+4”铝热连轧轧制力自学习[J]. 中国机械工程, 2013, 24(24): 3300-3303.

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