In order to improve the cold rolled steel strip flatness, the load distribution of the tandem cold rolling process is subject to investigation and optimization. The strip deformation resistance model is corrected by an artificial neural network that is trained with the actual measured data of 4500 strip coils. Based on the model, a flatness prediction model of strip steel is established in a five-stand tandem cold rolling mill, and the precision of the flatness prediction model is verified by rolling experiment data. To analyze the effect of load distribution on flatness, the flatness of stand 4 is calculated to be 7.4 IU, 10.6 IU, and 16.8 IU under three typical load distribution modes. A genetic algorithm based on the excellent flatness is proposed to optimize the load distribution further. In the genetic algorithm, the classification of flatness of stand 4 calculated by the developed flatness prediction model is taken as the fitness function, with the optimal reduction of 28.6%, 34.6%, 27.3%, and 18.6% proposed for stands 1, 2, 3, and 4, respectively. The optimal solution is applied to a 1740 mm tandem cold rolling mill, which reduce the flatness classification from 10.8 IU to 3.2 IU for a 1-mm thick steel strip.

中文翻译：

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冷连轧带钢轧制负荷分配的研究与优化

为了提高冷轧带钢的平直度，对串联冷轧过程的载荷分布进行研究和优化。通过人工神经网络对带钢变形抗力模型进行校正，该人工神经网络使用4500根带钢卷的实际测量数据进行训练。基于该模型，在五机架串联冷轧机中建立了带钢平直度预测模型，并通过轧制实验数据验证了平直度预测模型的精度。为了分析负载分布对平面度的影响，在三种典型的负载分布模式下，支架4的平面度经计算为7.4 IU，10.6 IU和16.8 IU。提出了一种基于优良平坦度的遗传算法，进一步优化了负荷分配。在遗传算法中 根据发达的平整度预测模型计算出的4号展台的平整度等级作为适应度函数，对1号，2号，3号和4号展台的最佳减少量分别为28.6％，34.6％，27.3％和18.6％。分别。最佳解决方案适用于1740毫米串联冷轧机，该设备将1毫米厚钢带的平直度等级从10.8 IU降低到3.2 IU。