摘要 Aiming at the problems of low accuracy and poor robustness that existed in the current hot rolling strip width spread model, an improved strip spread prediction model based on a material forming mechanism and Bayesian optimized adaptive differential evolution algorithm (BADE) was proposed. At first, we improved the original spread mechanism model by adding the weight and bias term to enhance the model robustness based on rolling temperature. Then, the BADE algorithm was proposed to optimize the improved spread mechanism model. The optimization algorithm is based on a novel adaptive differential evolution algorithm, which can effectively achieve the global optimal solution. Finally, the prediction performances of five machine learning algorithms were compared in experiments. The results show that the prediction accuracy of the improved spread model is obviously better than that of the machine learning algorithms, which proves the effectiveness of the proposed method.
Abstract：Aiming at the problems of low accuracy and poor robustness that existed in the current hot rolling strip width spread model, an improved strip spread prediction model based on a material forming mechanism and Bayesian optimized adaptive differential evolution algorithm (BADE) was proposed. At first, we improved the original spread mechanism model by adding the weight and bias term to enhance the model robustness based on rolling temperature. Then, the BADE algorithm was proposed to optimize the improved spread mechanism model. The optimization algorithm is based on a novel adaptive differential evolution algorithm, which can effectively achieve the global optimal solution. Finally, the prediction performances of five machine learning algorithms were compared in experiments. The results show that the prediction accuracy of the improved spread model is obviously better than that of the machine learning algorithms, which proves the effectiveness of the proposed method.
Yan-jiu Zhong,Jing-cheng Wang,Jia-hui Xu, et al. Strip width spread prediction in rough rolling process based on mechanism modeling and optimization[J]. Journal of Iron and Steel Research International, 2023, 30(12): 2416-2424.
2023, Vol. 30 
