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为提高二次冷轧兼平整机组在二次冷轧模式下轧制力的预报精度,建立了一种基于摩擦系数自学习的轧制力预报模型。考虑到摩擦系数自学习模型的不足,为进一步提高轧制力的预报精度,提出了一种支持向量回归预测轧制力的计算误差与摩擦系数自学习相结合的轧制力预报方法。结果表明,该模型的计算值与实际值吻合较好,误差控制在±7%以内,满足现场生产要求,具有较高的工程应用价值。
In order to improve the prediction accuracy of the rolling force of the secondary cold-rolling and flattening unit in the secondary cold-rolling mode, a rolling force prediction model based on the friction coefficient self-learning was established. In order to further improve the prediction accuracy of rolling force, a method of predicting the rolling force by combining the calculation error of the rolling force with the friction coefficient self-learning is proposed. The results show that the calculated value of the model is in good agreement with the actual value and the error is controlled within ± 7%, which meets the requirements of on-site production and has high engineering application value.