论文部分内容阅读
为了达到厚板生产中的强度和屈强比等性能指标,本文在用神经元网络对屈服强度和抗拉强度建模的基础上,结合粒子群优化算法对粗轧开轧温度、中间坯厚度、终轧温度、终冷温度及冷却速率等生产工艺参数进行了优化。优化结果与实验室热轧实验及工业试生产结果的对比表明,本模型能有效地优化厚板生产过程的工艺参数,从而为优化工艺或柔性化生产工艺的设计提供指导。
In order to achieve the performance index of strength and yield ratio in the production of thick slab, this paper, based on the modeling of yield strength and tensile strength by neural network, combined with particle swarm optimization algorithm, , Finishing temperature, final cooling temperature and cooling rate and other production process parameters were optimized. The comparison between the optimization results and laboratory hot rolling experiments and industrial pilot production shows that the model can effectively optimize the process parameters of the slab production process and provide guidance for the design of the optimization process or the flexible production process.