基于工况识别的辊式淬火过程板形预报方法

板形是衡量淬火后钢板质量的重要指标之一,板形的预报对高质量钢板的持续稳定生产具有重要的指导意义.本文提出一种基于工况识别的辊式淬火过程板形预报方法,为淬火生产控制决策提供参考依据.首先对淬火过程进行特性分析;然后采用模糊C均值聚类算法对淬火过程进行工况识别,使用支持向量机建立各工况的板形预报模型,并运用改进的粒子群优化算法提高模型的精度;最后利用工业生产数据进行实验,结果验证了本文所提方法的可行性与有效性.

Flatness prediction method based on operating mode recognition for roller quenching process

Flatness is an important indicator to measure the quality of quenched steel plate, and the prediction of flatness is of great significance for the continuous and stable production of high-quality steel plate. This paper proposes a method based on operating mode recognition to predict the flatness for the roller quenching process, which provides a reference for the quenching production control decision. Firstly, the characteristics of the quenching process are analyzed. Then the fuzzy C-means clustering algorithm is used to recognize the operating modes of the process, the support vector machine is used to establish the flatness prediction model for each operating mode, and the improved particle swarm optimization algorithm is applied to improve the accuracy of the model. Finally, experiments are performed using industrial production data, and the results verify the feasibility and effectiveness of the flatness prediction method proposed in this paper.